web app: "Use for other apps too" - the Chat settings as every client's defaults

A switch in the Sampling drawer. With it on, Apply stores the drawer's settings on the server (POST /settings): thinking level, temperature, top-p, top-k, seed, max tokens and the experimental speed projection. omp and other API clients then get them for anything they don't send themselves, from their next request (no restart); what a client sends still wins. Off again, clients use their own settings. - Kept between starts in <config>.shared-settings.json next to the run config; the server window says what other apps use. - Only Strata's own page can change them: JSON only (a cross-site form or simple request can't send it without a CORS preflight, which this server never grants), no foreign Origin, and the API key when one is set. - Values are checked (bad ones are a 400 naming the setting). - Tests: defaults reach the engine, a client's own values win, off clears them and the file, 415/403/400/401.
web app: a long switch label no longer squeezes the switch
2026-11-07 17:38:20 -10:00 · 2026-09-27 17:50:38 +02:00 · 2026-09-27 17:45:18 +02:00 · 2026-09-27 17:48:41 +02:00 · 2026-09-27 17:46:12 +02:00 · 2026-09-27 17:30:09 +02:00
47 changed files with 4460 additions and 374 deletions
@@ -46,3 +46,4 @@ build-vision.bat
 /dist/
 *.done
 /third_party/*.zip
+
@@ -178,7 +178,9 @@ if(STRATA_ENABLE_CUDA)
                          src/kernels/cuda/ple.cu
                          src/kernels/cuda/kv_q8.cu
                          src/kernels/cuda/kv_stream.cu
+                          src/kernels/cuda/kv_q4.cu
                          src/kernels/cuda/fused_gr.cu
+                          src/kernels/cuda/cvec.cu
                          src/kernels/cuda/qsa_decode_attn.cu
                          src/kernels/cuda/fused_gdn.cu
                          src/kernels/cuda/dequant_bf16.cu
@@ -363,6 +365,16 @@ if(STRATA_ENABLE_CUDA)
  target_link_libraries(kv_stream_parity PRIVATE strata_kernels CUDA::cudart)
  add_test(NAME kv_stream_parity COMMAND kv_stream_parity)

+  # PR #21: Q4_0 KV with Walsh-Hadamard rotation vs a host reference (GPU, synthetic, no model).
+  add_executable(kv_q4_parity src/kernels/kv_q4_parity.cpp)
+  target_include_directories(kv_q4_parity PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
+  target_link_libraries(kv_q4_parity PRIVATE strata_kernels CUDA::cudart)
+  add_test(NAME kv_q4_parity COMMAND kv_q4_parity)
+  add_executable(cvec_parity src/kernels/cvec_parity.cpp)
+  target_include_directories(cvec_parity PRIVATE ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
+  target_link_libraries(cvec_parity PRIVATE strata_kernels CUDA::cudart)
+  add_test(NAME cvec_parity COMMAND cvec_parity)
+
  # Plan v0.3 P3: native MMVQ for 1..8 tokens (bitwise selftest + --bench timing); synthetic data, no model.
  if(STRATA_BUILD_TESTS)
  add_executable(native_mmvq_multi bench/micro/native_mmvq_multi.cpp)
@@ -3,54 +3,22 @@
 <p align="center"><b>Run a 125-billion-parameter AI model on a normal gaming PC</b><br>
 one NVIDIA card (12-24 GB) + 64 GB of RAM · Windows or Linux · one click to install</p>

+<p align="center"><a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4"><img src="docs/media/pagoda-preview.webp" width="720" alt="A voxel pagoda garden that Strata's model wrote, running in the browser"></a><br>
+<sub>A voxel pagoda garden, 1 shot prompt running on an RTX 5070 with Strata (IQ3_S, 128K context) ·
+<a href="https://github.com/Niko1221/Strata/releases/download/v0.1.10/Pagoda.mp4">full video (49 s)</a></sub></p>
+
 Strata runs **[Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)** - a large, smart AI model that
 normally needs a server - on your own PC. It writes its answers at **60-95 tokens per second** (a token is about ¾
 of a word): faster than you can read.

- **Private:** everything runs on your PC. Nothing is sent anywhere.
- **Works with your apps:** chat apps, coding agents and scripts that speak the OpenAI or Anthropic API just work.
- **Sees pictures** too, if you want (screenshots, photos, scanned pages).
 - **Free and open source.**

-> **Jump to:** [Is my PC enough?](#is-my-pc-enough) · [Install](#install-3-steps) · [How fast?](#how-fast-is-it) ·
-> [Which model?](#which-model-should-i-pick) · [Using it](#using-it) · [Problems?](#something-went-wrong) ·
-> [How it works](#how-does-it-work) · [All the details](docs/DETAILS.md)
+> **Jump to:** [How fast?](#how-fast-is-it) · [Which model?](#which-model-should-i-pick) · [Install](#install) ·
+> [Using it](#using-it) · [Problems?](#something-went-wrong) · [How it works](#how-does-it-work) ·
+> [All the details](docs/DETAILS.md)

 ---

-## Is my PC enough?
-
-| You need | |
-| --- | --- |
-| **Graphics card** | NVIDIA RTX 30, 40 or 50 series with **12 GB of VRAM or more** |
-| **Memory (RAM)** | **64 GB** |
-| **Free disk space** | ~80 GB (an SSD makes the first start much faster) |
-| **System** | Windows 10/11, or Linux |
-
-That's it. The only thing you install yourself is a current **NVIDIA driver**
-([nvidia.com/drivers](https://www.nvidia.com/drivers) or the NVIDIA App). Everything else - Python, the engine, the
-model - is set up for you.
-
-## Install (3 steps)
-
-**Windows**
-
-1. [Download this project](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) and unzip it (or `git clone` it).
-2. Double-click **`START-HERE.bat`**.
-3. Answer 4 questions - or just press Enter each time for the recommended choice:
-   - **Which model?** The original, or Swift 1.5 (a version that thinks shorter and answers sooner)
-   - **Which size?** Q2_0, IQ2_XS, IQ3_XXS or IQ3_S - see [which model](#which-model-should-i-pick)
-   - **How much context?** How much text it can keep in mind at once (it suggests one for your card)
-   - **Images?** Whether it should also read pictures
-
-Then it downloads everything (the model is ~70 GB, so the first time takes a while - you can stop and it picks up
-where it left off) and **starts the model**. Your browser opens a chat page at `http://127.0.0.1:8080`.
-
-**Next time**, just double-click `START-HERE.bat` again: it starts right away, nothing is downloaded twice. Close its
-window to stop the model.
-
-**Linux:** run `./setup.sh` - same questions, same result.
-
 ## How fast is it?

 Measured on an RTX 5070 (12 GB), a Ryzen 5 7600 and 64 GB of RAM:
@@ -73,49 +41,105 @@ A card with more VRAM is faster, because more of the model fits on the GPU: an R

 **The size** (the same model, compressed more or less):

-| Size | Download | Speed | Quality | Pick it if... |
-| --- | ---: | --- | --- | --- |
-| **Q2_0** | 66 GB | fastest | good | you want speed |
-| **IQ2_XS** | 68 GB | fast | better | you want a good all-rounder (**recommended**) |
-| **IQ3_XXS** | 76 GB | slower | great | you want better answers (uses 43 GB of your 64 GB RAM) |
-| **IQ3_S** | 84 GB | slowest | best: matches the full model on the published tests | you want the very best answers (original model only; uses 50 GB of your 64 GB RAM, so close other big programs) |
+| Model | RAM+VRAM Requirements | Speed | Quality |
+| --- | ---: | --- | --- |
+| **Q2_0** | 37.6 GB | fastest | good |
+| **IQ2_XS** | 39.2 GB | fast | better (**recommended**) |
+| **IQ3_XXS** | 47.0 GB | slower | great |
+| **IQ3_S** | 54.8 GB | slowest | best: matches the full model on the published tests (original model only) |
+
+**Will it fit?** Shard 1 is the part of the model that gets loaded when it starts: its experts go into your **RAM**,
+the rest onto your graphics card (the second shard, a 29 GB lookup table, stays on the SSD). So it fits when your
+**RAM is at least shard 1 + about 10 GB** for Windows and your other programs. With 64 GB of RAM every size fits
+(IQ3_S with little else open); with 48 GB, Q2_0 and IQ2_XS. A bigger graphics card makes it faster, but it doesn't
+lower the RAM needed.

 **The version:**

 - **Qwen3.8-Flash-Next** - the original.
 - **[Swift 1.5](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF)** - a fine-tune by UkisAI
  that thinks much shorter before answering, so you get the answer sooner, with about the same quality. Same speed per
-  token. Its own license applies (see its page).
+  token, and about the same RAM as the same size of the original (no IQ3_S). Its own license applies (see its page).

 Not sure? Take **IQ2_XS**. You can add another one later with `START-HERE.bat --setup`.

+## Install
+
+**You need:** an NVIDIA RTX 30, 40 or 50 card with 12 GB of VRAM or more, enough RAM for the size you pick (above),
+~80 GB of free disk space (an SSD makes the first start much faster), and Windows 10/11 or Linux. The only thing you
+install yourself is a current **NVIDIA driver** ([nvidia.com/drivers](https://www.nvidia.com/drivers) or the NVIDIA
+App). Everything else - Python, the engine, the model - is set up for you.
+
+**Windows**
+
+1. [Download this project](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) and unzip it (or `git clone` it).
+2. Double-click **`START-HERE.bat`**.
+3. Answer a few questions - or just press Enter each time for the recommended choice:
+   - **Which model and size?** The original or Swift 1.5, and Q2_0, IQ2_XS, IQ3_XXS or IQ3_S - see [above](#which-model-should-i-pick)
+   - **How much context?** How much text it can keep in mind at once (it suggests one for your card)
+   - **Images?** Whether it should also read pictures
+   - **Experimental speed projection?** Off unless you say yes - [read what it does](docs/DETAILS.md#experimental-speed-projection-experimental-off-by-default) first
+
+Then it downloads everything (the model is ~70 GB, so the first time takes a while - you can stop and it picks up
+where it left off) and **starts the model**. Your browser opens the Strata app at `http://127.0.0.1:8080`.
+
+**Next time**, just double-click `START-HERE.bat` again: it starts right away, nothing is downloaded twice. Close its
+window to stop the model.
+
+**Linux:** run `./setup.sh` - same questions, same result.
+
 ## Using it

- **Chat in the browser:** `http://127.0.0.1:8080` - a simple chat page (it opens by itself when the model starts).
+<p align="center"><img src="docs/media/runpagoda.png" width="900" alt="The Strata app's Monitor tab next to a coding agent"><br>
+<sub>The Strata app's <b>Monitor</b> (left) while a coding agent writes the pagoda garden from the video (right)</sub></p>
+
+- **In the browser:** `http://127.0.0.1:8080` - the Strata app (it opens by itself when the model starts): **Chat**, a
+  live **Monitor** of the model and your GPU/CPU/RAM, and **About** with the settings and addresses.
 - **Chat in the terminal:** `.venv\Scripts\python chat.py`
 - **Your apps and coding agents:** add it as an "OpenAI-compatible" provider with base URL
  **`http://127.0.0.1:8080/v1`**, any API key and any model name. Apps that use Anthropic's API: `http://127.0.0.1:8080/v1/messages`.
 - **Thinking:** the model thinks before it answers. Choose **off, low, medium or high** - in the chat page menu, with
  `/think low` in `chat.py`, or with your app's "reasoning effort" setting. Off is fastest; high is best for hard questions.
 - **Pictures:** in the chat page click **Picture**; in `chat.py` type `/image <path>`; in apps just attach them.
- **From your phone or another PC:** see the [details](docs/DETAILS.md#using-it) (set an API key first).
+- **From your phone or another PC:** `START-HERE.bat --setup --host 0.0.0.0 --api-key <secret>`, then open the
+  address the server window prints; see the [details](docs/DETAILS.md#using-it).
+- **Experimental speed projection (off by default):** an experimental control vector that setup can turn on; it
+  changes how the model answers - read [what it does](docs/DETAILS.md#experimental-speed-projection-experimental-off-by-default) first.

 **Good to know:** it answers one request at a time. The first message of a chat is read in full (about 1 minute per
 30,000 tokens); after that it keeps the conversation and reads only what is new, so follow-ups start in seconds.

 ## Something went wrong?

-| What you see | What to do |
-| --- | --- |
-| `the NVIDIA driver is too old` | Update the driver (NVIDIA App or nvidia.com/drivers), restart the PC, run `START-HERE.bat` again. |
-| It stopped during download or setup | Run `START-HERE.bat` again - it continues where it stopped. |
-| `port 8080 is already in use` | Strata is already running - look for its window. |
-| The first start takes minutes | Normal: it loads 35-43 GB into RAM. The next start is faster. |
-| Slow, and the disk light is busy | Not enough free RAM: close other programs (browsers use a lot), or pick Q2_0 / IQ2_XS. |
-| "prompt exceeds the context" | The conversation is longer than the context you chose: run `START-HERE.bat --setup` and pick more. |
+**My PC froze, or got very slow, the first time Strata started.**
+That's normal the first time. Strata loads 35-55 GB into your RAM, locks part of it for the graphics card, and works
+out how much of the model fits on your GPU. The mouse can freeze for a few minutes. **Wait, and don't close the
+window.** The next starts are much faster. Still frozen after 10 minutes? Restart the PC, close other programs
+(browsers use a lot of RAM) and try again. If it keeps happening, pick a smaller size (Q2_0 or IQ2_XS).

-More in the [full troubleshooting table](docs/DETAILS.md#troubleshooting). Still stuck? Open an issue and attach
-`strata-<model>.log` from this folder.
+**It stopped while downloading or installing.**
+Run `START-HERE.bat` again. It continues where it stopped.
+
+**It says the NVIDIA driver is too old.**
+Update it (NVIDIA App or [nvidia.com/drivers](https://www.nvidia.com/drivers)), restart the PC, and run
+`START-HERE.bat` again.
+
+**It says port 8080 is already in use.**
+Strata is already running. Look for its window.
+
+**It's very slow and the disk light keeps blinking.**
+Your PC is out of free RAM. Close other programs, or pick a smaller size (Q2_0 or IQ2_XS).
+
+**An answer stopped with "the engine stopped unexpectedly".**
+Usually not enough RAM (on Linux the system then stops the engine). Just send your message again: Strata starts the
+engine by itself. If it keeps happening, close other programs or pick a smaller size.
+
+**It says the prompt exceeds the context.**
+The conversation is longer than the context you chose. Start a new chat, or run `START-HERE.bat --setup` and pick more
+context.
+
+**Still stuck?** Look in the [full troubleshooting table](docs/DETAILS.md#troubleshooting), or open an issue and
+attach `strata-<model>.log` from the Strata folder.

 ## How does it work?

@@ -0,0 +1,29 @@
+# 2026-09-27: expert cache on vs off (issue #23)
+
+Question: the engine used to warn at start that "the GPU hit path is NOT CORRECT ... any OUTPUT from it is not". Does
+the expert cache make the output worse?
+
+Method: teacher-forced. `strata --dump-logits` runs a text one token at a time and writes the logits at every
+position, so both runs see the same text and each position's next-token prediction can be compared:
+- cache off: no `--expert-cache`, every routed expert computed by the CPU pool;
+- cache on: `--expert-profile pack/profile-decode-8k.bin --expert-cache auto`, 5,130 of the most-used experts on the GPU.
+
+Q2_0, RTX 5070, the first 512 or 1,024 tokens of three frozen prompts.
+
+| Text | Same top-1 | Median KL | Perplexity off | Perplexity on | On - off (nats per token) |
+|---|---:|---:|---:|---:|---:|
+| Code agent (512) | 97.7% | 3.0e-04 | 2.672 | 2.630 | -0.016 +- 0.019 |
+| Long document (1,024) | 96.2% | 4.5e-03 | 29.844 | 29.843 | -0.000 +- 0.005 |
+| Chat (1,024) | 95.0% | 4.4e-03 | 18.380 | 18.301 | -0.004 +- 0.006 |
+| **All 2,557 tokens** | | | | | **-0.005 +- 0.005** |
+
+Reading:
+- The cache changes which of two nearly equal candidates wins at 2-5% of positions. Most of the flips have a top-2
+  margin under 0.5 logits (`logits-compare.txt`).
+- Neither side is more accurate: the perplexity of the actual text is the same, within 1 standard error.
+- A GPU expert and the CPU compute the same quantized expert with a different floating-point order. Both are an
+  equally good approximation of the model.
+
+The start-up warning dates from round 328, when the GPU hit path had a real fault (divergence from token 0). That fault
+was fixed long ago and is covered by `expert_parity`, `native_expert_parity` and the grouped kernels' tests. Since
+engine 0.1.8 the warning is replaced by one accurate line.
@@ -0,0 +1,36 @@
+# 2026-09-27: the experimental-speed-projection control vector
+
+The package's `Qwen3.8-Flash-Next-experimental-speed-projection.gguf` (47 unit directions of 2,560, one per layer),
+applied as `--control-vector-scaled FILE:1.0 --control-vector-layer-range 4 44 --cvec-mode project --cvec-dir
+per-layer`: after layers 4-44, every hyper-connection stream `h -= (h . v) v`. The package describes it as a
+refusal-direction projection. Q2_0, RTX 5070, fixed experts (`--expert-cache 4000 --pcie-frac 0 --adapt-swaps 0`).
+
+## Kernel (`cvec_parity`)
+
+Project and add against a double-precision reference (max error 5e-7); with the switch off, the kernel's pending
+residual write is bitwise the fused hyper-connection read's own, so a loaded-but-off vector is the stock engine.
+
+## Teacher-forced, stock vs projection (`esp_kl.py`, per-token path)
+
+| Text | Tokens | Same top-1 | Mean KL | p99 KL | Perplexity stock | With the vector |
+|---|---:|---:|---:|---:|---:|---:|
+| Code | 511 | 92.8% | 0.098 | 1.69 | 2.594 | 2.982 |
+| Document | 1,023 | 89.8% | 0.058 | 0.84 | 30.299 | 30.984 |
+| Chat | 1,023 | 89.6% | 0.050 | 0.33 | 18.409 | 18.485 |
+| **All** | 2,557 | | **0.063** (max 4.10) | 1.00 | | |
+
+The package reports a mean KL of 0.186 on its own neutral text with UD-Q2_K_XL in llama.cpp.
+
+Cost on the same tokens: 44.34 -> 44.26, 46.74 -> 46.56, 47.02 -> 46.83 tok/s (0.2-0.4%).
+
+## Serve (`esp_serve.py`)
+
+- Loaded and switched off (`cvec=0`) vs an engine without it: 65 / 65 first tokens after prefixes (verify windows
+  and the batched prompt path) and three 256-token generations **token-identical**.
+- On, off, on again: identical (the switch drops the conversation cache, so nothing computed one way is reused the
+  other way).
+- The prompt paths steer like the per-token path: served-with-vector first tokens agree with the per-token path's
+  teacher-forced argmax with the vector 62 / 65, and with the stock one 57 / 65 (stock served: 64 / 65 and 57 / 65).
+- Decode tok/s of 3 x 256-token generations: stock 66.4, on 76.7 / 81.2. **Not a like-for-like comparison:** the
+  model writes different text with it on (on the code prompt it copied a long stretch of the source, which prompt
+  lookup drafts at 91% acceptance, where stock stopped after 78 tokens). On the same tokens it is 0.2-0.4% slower.
@@ -0,0 +1,85 @@
+"""Teacher-forced logits, stock vs the experimental-speed-projection vector, on the same texts and fixed experts.
+
+    python esp_kl.py run      # the dumps (per-token path)
+    python esp_kl.py compare  # same top-1, mean/median KL(stock || projection), perplexities
+"""
+import json
+import os
+import subprocess
+import sys
+
+import numpy as np
+
+S = os.path.dirname(os.path.abspath(__file__))
+E = r"C:\Users\AI-Server\Desktop\Strata\Public\Engine"
+EXE = E + r"\build-dev\strata.exe"
+VEC = r"C:\Users\AI-Server\Desktop\Strata\experimental-speed-projection\Qwen3.8-Flash-Next-experimental-speed-projection.gguf"
+TEXTS = {"code": S + r"\p512.ids", "doc": S + r"\4k-02-long-doc-1k.ids", "chat": S + r"\4k-04-chat-1k.ids"}
+OUT = S + r"\esp-kl"
+ARMS = {"stock": [],
+        "proj": ["--control-vector-scaled", VEC + ":1.0", "--control-vector-layer-range", "4", "44",
+                 "--cvec-mode", "project", "--cvec-dir", "per-layer"]}
+
+
+def base_args():
+    c = json.load(open(S + r"\cfg-rel.json"))
+    a = list(c["args"])
+    i = a.index("--expert-cache")
+    a[i + 1] = "4000"
+    j = a.index("--prefill")          # the prompt one token at a time: a logits row per position
+    del a[j:j + 2]
+    return a + ["--pcie-frac", "0", "--adapt-swaps", "0"], c
+
+
+def run():
+    os.makedirs(OUT, exist_ok=True)
+    args, c = base_args()
+    env = dict(os.environ)
+    env["PATH"] = os.pathsep.join(c["lib_dirs"]) + os.pathsep + env["PATH"]
+    for name, path in TEXTS.items():
+        for arm, extra in ARMS.items():
+            out = f"{OUT}\\{name}-{arm}.bin"
+            if os.path.exists(out):
+                continue
+            cmd = [EXE, *args, *extra, "--tokens-file", path, "--max-new", "1", "--dump-logits", out]
+            with open(f"{OUT}\\{name}-{arm}.log", "w") as log:
+                r = subprocess.run(cmd, cwd=c["cwd"], stdout=log, stderr=subprocess.STDOUT, env=env)
+            print(name, arm, "exit", r.returncode, flush=True)
+
+
+def load(p):
+    h = np.fromfile(p, dtype=np.int32, count=2)
+    return np.memmap(p, dtype=np.float32, mode="r", offset=8, shape=(int(h[1]), int(h[0])))
+
+
+def lsm(x):
+    x = x.astype(np.float64)
+    m = x.max(axis=-1, keepdims=True)
+    return x - (m + np.log(np.exp(x - m).sum(axis=-1, keepdims=True)))
+
+
+def compare():
+    print(f"{'text':<6}{'tokens':>7}{'same top-1':>11}{'mean KL':>9}{'median KL':>11}{'p99 KL':>8}{'ppl stock':>10}{'ppl proj':>9}")
+    allkl = []
+    for name, path in TEXTS.items():
+        ids = [int(x) for x in open(path).read().replace(",", " ").split()]
+        a, b = load(f"{OUT}\\{name}-stock.bin"), load(f"{OUT}\\{name}-proj.bin")
+        n = min(a.shape[0], b.shape[0], len(ids) - 1)
+        kl, same, la, lb = [], 0, [], []
+        for r in range(0, n, 64):
+            pa, pb = lsm(a[r:min(n, r + 64)]), lsm(b[r:min(n, r + 64)])
+            kl += list((np.exp(pa) * (pa - pb)).sum(axis=1))
+            same += int((pa.argmax(1) == pb.argmax(1)).sum())
+            nxt = np.array(ids[r + 1:min(n, r + 64) + 1])
+            la += list(pa[np.arange(len(nxt)), nxt])
+            lb += list(pb[np.arange(len(nxt)), nxt])
+        kl = np.array(kl)
+        allkl += list(kl)
+        print(f"{name:<6}{n:>7}{100 * same / n:>10.1f}%{kl.mean():>9.3f}{np.median(kl):>11.4f}{np.percentile(kl, 99):>8.2f}"
+              f"{np.exp(-np.mean(la)):>10.3f}{np.exp(-np.mean(lb)):>9.3f}")
+    allkl = np.array(allkl)
+    print(f"all {len(allkl)} tokens: mean KL {allkl.mean():.3f}, max {allkl.max():.2f}, p99 {np.percentile(allkl, 99):.2f}")
+
+
+if __name__ == "__main__":
+    {"run": run, "compare": compare}[sys.argv[1]]()
@@ -0,0 +1,129 @@
+"""Serve-mode checks of the experimental-speed-projection vector (exact mode: fixed experts, no PCIe share).
+
+    python esp_serve.py run ARM      # ARM = stock | vec; writes esp-serve/<arm>.json
+    python esp_serve.py compare
+
+Per arm: the first greedy token after many prefixes of three texts (short ones go through the verify windows, long
+ones through the batched prompt path; the conversation cache reuses the earlier prefix), and three 256-token greedy
+generations.  The vec arm runs everything with cvec=1, then cvec=0, then cvec=1 again.
+"""
+import json
+import os
+import sys
+import threading
+
+S = os.path.dirname(os.path.abspath(__file__))
+E = r"C:\Users\AI-Server\Desktop\Strata\Public\Engine"
+sys.path.insert(0, E)
+from serve.server import StrataEngine  # noqa: E402
+
+EXE = E + r"\build-dev\strata.exe"
+VEC = r"C:\Users\AI-Server\Desktop\Strata\experimental-speed-projection\Qwen3.8-Flash-Next-experimental-speed-projection.gguf"
+TEXTS = {"code": S + r"\p512.ids", "doc": S + r"\4k-02-long-doc-1k.ids", "chat": S + r"\4k-04-chat-1k.ids"}
+OUT = S + r"\esp-serve"
+VEC_ARGS = ["--control-vector-scaled", VEC + ":1.0", "--control-vector-layer-range", "4", "44",
+            "--cvec-mode", "project", "--cvec-dir", "per-layer"]
+
+
+def ids_of(name):
+    return [int(x) for x in open(TEXTS[name]).read().replace(",", " ").split()]
+
+
+def engine(extra):
+    c = json.load(open(S + r"\cfg-rel.json"))
+    a = list(c["args"])
+    a[a.index("--expert-cache") + 1] = "4000"
+    env = dict(os.environ)
+    env["PATH"] = os.pathsep.join(c["lib_dirs"]) + os.pathsep + env["PATH"]
+    return StrataEngine(EXE, a + ["--pcie-frac", "0", "--adapt-swaps", "0"] + extra, cwd=c["cwd"],
+                        log=OUT + r"\engine.log", env=env)
+
+
+def gen(eng, ids, n, sampling):
+    toks = [t for t in eng.generate(ids, n, sampling, threading.Event()) if t is not None]
+    return toks, dict(eng.last)
+
+
+def suite(eng, sampling):
+    out = {"first": {}, "long": {}}
+    for name in TEXTS:
+        ids = ids_of(name)
+        # short prefixes growing by 4 (each continues the live session through the verify windows), then long ones
+        # SHRINKING (no cached prefix, so each is read from 0 by the batched prompt path)
+        lens = list(range(32, 72, 4)) + list(range(min(len(ids) - 1, 700), 95, -48))
+        out["first"][name] = {L: gen(eng, ids[:L], 1, sampling)[0][0] for L in lens}
+    for name in TEXTS:
+        toks, last = gen(eng, ids_of(name)[:300], 256, sampling)
+        out["long"][name] = {"tokens": toks, "last": last}
+    return out
+
+
+def run(arm):
+    os.makedirs(OUT, exist_ok=True)
+    eng = engine(VEC_ARGS if arm == "vec" else [])
+    res = {"info": eng.info}
+    if arm == "stock":
+        res["stock"] = suite(eng, {})
+    else:
+        res["on"] = suite(eng, {"experimental_speed_projection": True})
+        res["off"] = suite(eng, {"experimental_speed_projection": False})
+        res["on2"] = suite(eng, {"experimental_speed_projection": True})
+    eng.close()
+    json.dump(res, open(f"{OUT}\\{arm}.json", "w"))
+    print(arm, "done", eng.info, flush=True)
+
+
+def compare():
+    import numpy as np
+    sys.path.insert(0, S)
+    from esp_kl import load, OUT as KL
+    st = json.load(open(OUT + r"\stock.json"))
+    ve = json.load(open(OUT + r"\vec.json"))
+    print("engine INFO (vec):", ve["info"])
+
+    def eq_first(a, b):
+        tot = same = 0
+        for name in a:
+            for L, t in a[name].items():
+                tot += 1
+                same += int(b[name][L] == t)
+        return same, tot
+
+    def eq_long(a, b):
+        return all(a[n]["tokens"] == b[n]["tokens"] for n in a), \
+            [(n, next((i for i, (x, y) in enumerate(zip(a[n]["tokens"], b[n]["tokens"])) if x != y), None)) for n in a]
+
+    print("1. vector loaded, switched off == no vector")
+    print("   first tokens: %d / %d identical" % eq_first(st["stock"]["first"], ve["off"]["first"]))
+    print("   256-token generations identical:", *eq_long(st["stock"]["long"], ve["off"]["long"]))
+    print("2. repeatable: on, off, on again")
+    print("   first tokens: %d / %d identical" % eq_first(ve["on"]["first"], ve["on2"]["first"]))
+    print("   256-token generations identical:", *eq_long(ve["on"]["long"], ve["on2"]["long"]))
+    print("3. the prompt paths against the per-token path's teacher-forced argmax (row L-1 predicts token L)")
+    for arm, first in (("stock", st["stock"]["first"]), ("on", ve["on"]["first"])):
+        for ref in ("stock", "proj"):
+            agree = tot = 0
+            for name in first:
+                d = load(f"{KL}\\{name}-{ref}.bin")
+                for L, t in first[name].items():
+                    L = int(L)
+                    if L - 1 < d.shape[0]:
+                        tot += 1
+                        agree += int(int(np.argmax(d[L - 1])) == t)
+            print(f"   served {arm:<5} vs per-token {ref:<5}: {agree} / {tot}")
+    print("4. how different the text is: 256-token generations, on vs stock, first difference")
+    print("  ", eq_long(st["stock"]["long"], ve["on"]["long"])[1])
+    print("5. decode speed (exact mode: fixed experts, no PCIe share - not the normal setup)")
+    for label, r in (("stock", st["stock"]), ("vec off", ve["off"]), ("vec on", ve["on"]), ("vec on 2", ve["on2"])):
+        g = sum(v["last"]["generated"] for v in r["long"].values())
+        ms = sum(v["last"]["decode_ms"] for v in r["long"].values())
+        acc = sum(v["last"].get("drafts_accepted", 0) for v in r["long"].values())
+        off = sum(v["last"].get("drafts_offered", 0) for v in r["long"].values())
+        print(f"   {label:<9} {g / ms * 1000:6.1f} tok/s  drafts accepted {acc}/{off}")
+
+
+if __name__ == "__main__":
+    if sys.argv[1] == "run":
+        run(sys.argv[2])
+    else:
+        compare()
@@ -0,0 +1,20 @@
+1. vector loaded, switched off == no vector
+   first tokens: 65 / 65 identical
+   256-token generations identical: True [('code', None), ('doc', None), ('chat', None)]
+2. repeatable: on, off, on again
+   first tokens: 65 / 65 identical
+   256-token generations identical: True [('code', None), ('doc', None), ('chat', None)]
+3. the prompt paths against the per-token path's teacher-forced argmax (row L-1 predicts token L)
+   served stock vs per-token stock: 64 / 65
+   served stock vs per-token proj : 57 / 65
+   served on    vs per-token stock: 57 / 65
+   served on    vs per-token proj : 62 / 65
+4. how different the text is: 256-token generations, on vs stock, first difference
+   [('code', 11), ('doc', 6), ('chat', 3)]
+5. decode speed (exact mode: fixed experts, no PCIe share - not the normal setup)
+   stock       66.4 tok/s  drafts accepted 256/309
+   vec off     65.7 tok/s  drafts accepted 251/318
+   vec on      76.7 tok/s  drafts accepted 366/425
+   vec on 2    81.2 tok/s  drafts accepted 366/423
+
+[exited with code 0]
@@ -0,0 +1,6 @@
+text   tokens same top-1  mean KL  median KL  p99 KL ppl stock ppl proj
+code      511      92.8%    0.098     0.0031    1.69     2.594    2.982
+doc      1023      89.8%    0.058     0.0271    0.84    30.299   30.984
+chat     1023      89.6%    0.050     0.0265    0.33    18.409   18.485
+all 2557 tokens: mean KL 0.063, max 4.10, p99 1.00
+
@@ -0,0 +1,60 @@
+# 2026-09-27: Q4_0 KV with Hadamard rotation (PR #21, `--kv q4_0`)
+
+PR #21 (code-martin) adds a 4-bit K/V format: an orthonormal 256-point Walsh-Hadamard rotation of K, V and Q, then
+ggml `q4_0` blocks (576 B per cell and layer, vs 1,056 B in int8).
+
+## Integration fixes made on merge
+
+- **Q4 + KV streaming (`--kv-resident`).** The PR had no host copy for Q4 and skipped the staging, so with streaming
+  on, the prompt path wrote K/V through the residency map's -1 entries (out-of-bounds writes).
+  - Q4 is now a third format in `kv_stream`: host copy, block residency, prompt-path staging, MTP ring and STATE_HASH.
+  - `kv_stream_parity` checks it bitwise, streamed vs resident, with ring restore.
+- **Duplicate attention.** The per-token decode path ran attention twice, the first time before the selection's
+  blocks were made resident; it now runs once.
+- **Tie-break.** The group maximum's tie-break is now deterministic across lanes: equal |x| with opposite signs
+  could give one block two scales.
+- **Unrelated changes dropped:** duplicate path fixes (already in 0.1.7), personal `.gitignore` entries, and a
+  re-encoded CMakeLists.
+
+`kv_q4_parity` (the PR's test) and all 70 ctest tests pass.
+
+## Quality (teacher-forced, `tools/kv_precision_compare.py`)
+
+Same text, same fixed expert set (Q2_0, 3,000 slots, `--adapt-swaps 0`); only `--kv` differs. fp16 is the reference.
+
+| Text | KV | Same top-1 as fp16 | Mean KL | Perplexity | vs fp16 (nats/token) |
+|---|---|---:|---:|---:|---:|
+| code agent (512) | int8 | 98.4% | 0.014 | 2.649 | -0.011 +- 0.006 |
+| | q4_0 | 95.7% | 0.030 | 2.598 | -0.031 +- 0.014 |
+| document (1,024) | int8 | 95.6% | 0.009 | 30.18 | +0.012 +- 0.005 |
+| | q4_0 | 91.3% | 0.039 | 32.26 | **+0.078 +- 0.011** |
+| chat (1,024) | int8 | 95.5% | 0.009 | 18.24 | -0.003 +- 0.005 |
+| | q4_0 | 90.7% | 0.028 | 18.52 | +0.012 +- 0.008 |
+| document, 8K (every 8th position) | int8 | 92.3% | 0.114 | 7.63 | -0.013 +- 0.020 |
+| | q4_0 | 88.0% | 0.297 | 8.65 | **+0.112 +- 0.037** |
+
+- **int8 is indistinguishable from fp16** (within about 2 standard errors everywhere).
+- **q4_0 costs measurable precision, and more as the context grows:**
+  - document perplexity +8% at 1K and +12% at 8K;
+  - the KL from fp16 is 2.6-4.2x int8's;
+  - code and chat are near-neutral.
+- **Needles with `--kv q4_0 --kv-resident 32768`: 5/5** (1K, 32K, 128K at 10% and 90% depth, 262K;
+  `needles-q4.*`). Retrieval holds.
+
+## Speed and memory (Q2_0, 128K, matrix bench, `--kv-resident 32768`)
+
+| KV | Expert slots | Decode tok/s | Tokens/round | Rounds/s | K/V in RAM |
+|---|---:|---:|---:|---:|---:|
+| int8 (`kv-stream/matrix-stream`) | 4,044 | 62.4 | 2.52 | 24.8 | 1.52 GiB |
+| q4_0 (`matrix-q4`) | 4,190 | 67.4 | 2.62 | 25.7 (+3.7%) | 0.83 GiB |
+
+Without streaming (contexts up to 64K, or too little RAM for it) q4_0 halves the whole KV cache in VRAM instead.
+
+## Decision
+
+`--kv q4_0` is a user option:
+- `START-HERE.bat --kv q4_0`, or the question setup asks above 8K context;
+- or `--kv q4_0` in a `strata-*.json`.
+
+int8 stays the default: every published quality number was measured with it, and q4_0's precision cost is real,
+though it passes the needles.
@@ -0,0 +1,26 @@
+{
+ "mode": "project",
+ "dir": "per-layer",
+ "scale": 1.0,
+ "layer_range": [
+  4,
+  44
+ ],
+ "refusal_harmful_thinkoff": 2.0,
+ "refusal_harmful_thinkon": 2.0,
+ "refusal_harmless_thinkoff": 4.0,
+ "refusal_harmless_thinkon": 6.0,
+ "broken": 0,
+ "kl_mean": 0.18598,
+ "kl_max": 18.26549,
+ "kl_p99": 3.143571,
+ "decode_tps": 51.19,
+ "vector_file": "Qwen3.8-Flash-Next-experimental-speed-projection.gguf",
+ "base_model_gguf": "unsloth/Qwen3.8-Flash-Next-GGUF UD-Q2_K_XL",
+ "base_model_sha256_shard1": "a4f3b21e77353999829f2f767e9ac21ce9c71d29a74f2cc9eda48c9bf23c8b86",
+ "worktree_commit": "37e026e3b",
+ "criterion_status": "PASS",
+ "dirty_direction": false,
+ "note": "projection ablation h -= s*(h.v)*v, v unit-normalized per layer. dir=per-layer",
+ "branch": "cvec-hooks (github.com/Cudila/llama.cpp)"
+}
@@ -0,0 +1,21 @@
+# experimental-speed-projection (EXPERIMENTAL, off by default)
+
+`Qwen3.8-Flash-Next-experimental-speed-projection.gguf` is a 480 KB control vector for Qwen3.8-Flash-Next: one unit
+direction of 2,560 values per layer (`direction.1` .. `direction.47`, GGUF `controlvector` format). The JSON beside it
+is the configuration its authors selected and their scores.
+
+**What it does.** Turned on, Strata removes each direction from the residual stream after layers 4-44
+(`h -= (h . v) v` on every hyper-connection stream), the same operation as llama.cpp's `--cvec-mode project` in the
+package's patches. The package's own documentation describes the vector as a **refusal-direction projection**: the
+model declines far fewer requests (it reports 1 of 50 vs 50 of 50 on its test set), and removing refusals removes a
+safety behaviour - you are responsible for what the model writes with it on. It also shifts the model's predictions
+slightly on ordinary text (measured here: `bench/results/2026-09-27-esp/`). It is not an optimization in the engine:
+on the same text it costs 0.2-0.4% per token; a chat's speed with it on depends on the text the model writes.
+
+**Turning it on.** `START-HERE.bat --setup` asks (default: off), or pass `--experimental-speed-projection on`. With it
+loaded, the web app's Sampling drawer and the API field `"experimental_speed_projection": false` switch it off per
+request. See `docs/DETAILS.md`, "Experimental speed projection".
+
+**Origin and license.** A local package of the vector (the published original uses filenames containing "refusal";
+the bytes are the same), made from Qwen3.8-Flash-Next activations, so the Qwen Community License 1.0 of the model
+applies to it. Made for the original Qwen3.8-Flash-Next (not Swift 1.5).
@@ -42,6 +42,11 @@ attention reads in VRAM (`--kv-resident 32768`), so more experts fit on the GPU.
 costs ~13.7 KB of RAM per context token (1.7 GB at 128K). Existing installs: run `START-HERE.bat --setup` once to turn
 it on.

+**4-bit KV cache (engine 0.1.8, optional):** `START-HERE.bat --setup` asks above 8K context (or pass `--kv q4_0`). It
+halves the KV cache's memory with a Hadamard rotation before 4-bit rounding (PR #21), about 4% faster at 128K, but it
+is measurably less precise on long documents (perplexity +8-12%; needle tests still pass). 8-bit stays the default.
+Details: [`bench/results/2026-09-27-kv-q4`](../bench/results/2026-09-27-kv-q4/README.md).
+
 Time to first token is prompt length / prompt speed: about 7 s at 4K, 55 s at 32K, 4 minutes at 128K and 9 minutes at
 262K. The raw numbers: [`bench/results/`](../bench/results/). The [paper](paper/Strata-Paper.pdf) explains every number.

@@ -125,8 +130,16 @@ Windows, `build-essential` + CUDA on Ubuntu) and compiles the engine for your GP
 4. **Images?** yes / no (see [Images](#images-vision)).

 Then it downloads and prepares everything (the model is 66-76 GB, so the first start takes a while; an interrupted
-download continues where it stopped) and **starts the model**: your browser opens `http://127.0.0.1:8080`, a small
-page that shows it is running and lets you chat. The API is at `http://127.0.0.1:8080/v1` for your apps.
+download continues where it stopped) and **starts the model**: your browser opens `http://127.0.0.1:8080`, the Strata
+app. It has three tabs:
+- **Chat:** streaming answers, the model's thinking (folded away once it answers), code with a copy button, pictures when
+  images are on, and sampling and thinking-level settings. Chats stay in your browser.
+- **Monitor:** what the model is doing (reading the prompt, with progress, or writing, at how many tokens/s); GPU load,
+  VRAM, temperature, power and PCIe traffic; CPU, RAM and disk; the context in use; the last requests.
+- **About:** the model and engine settings, and the addresses to connect other apps.
+
+`http://127.0.0.1:8080/?q=your question` opens it with a new chat already asking. The API is at
+`http://127.0.0.1:8080/v1` for your apps.

 **Every time after that**, `START-HERE.bat` just starts the model (30-90 s to load 34-43 GB into RAM). Nothing is
 downloaded again. Closing the window stops the model.
@@ -171,6 +184,7 @@ The server listens on `http://127.0.0.1:8080` (change with `--port` in setup, or
 | Anthropic Messages (stream and non-stream, tools) | `POST /v1/messages` |
 | Model list / health | `GET /v1/models`, `GET /health` |
 | What the model is doing right now | `GET /status` |
+| Everything the Monitor tab shows (engine, live state, last requests, hardware) | `GET /metrics` |

 ```bash
 curl http://127.0.0.1:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
@@ -202,9 +216,18 @@ print(r.choices[0].message.content)
  15 s, and `GET /status` says what it is doing (`reading the prompt`, `answering`, tokens so far). Closing the
  connection or pressing stop in your app really stops the model, so the next request starts at once.
 - **Chat apps.** Any app with an "OpenAI-compatible" provider works: base URL `http://127.0.0.1:8080/v1`, any API key.
- **Context.** Chosen in setup (8K-262K). Requests longer than that are refused, never silently cut.
- **From other devices / the internet.** The server listens on your PC only (`127.0.0.1`). To reach it from elsewhere,
-  put a tunnel in front of it, for example [cloudflared](https://developers.cloudflare.com/cloudflare-one/connections/connect-networks/do-more-with-tunnels/trycloudflare/):
+- **Context.** Chosen in setup (8K-262K). Requests longer than that are refused, never silently cut. A request whose
+  `max_tokens` would run past the context is refused too (400); agents that always ask for their full output cap
+  can instead get it shortened to the room left: add `"fit_max_tokens": true` to `strata-<model>.json` (or pass
+  `--fit-max-tokens` to `serve/server.py`). A prompt that leaves no room at all is still refused.
+- **From other devices on your network.** The server listens on your PC only (`127.0.0.1`) unless you say otherwise:
+  run setup with `START-HERE.bat --setup --host 0.0.0.0 --api-key some-long-secret` (or add `"host": "0.0.0.0"` and
+  `"api_key": "..."` to `strata-<model>.json`). The server window then prints this PC's addresses
+  (`from other devices: http://192.168.x.x:8080/`); open that on the other device, or use `.../v1` as an API base URL.
+  On Windows the firewall blocks it until you allow it: accept its prompt for Python (private networks), or run
+  `New-NetFirewallRule -DisplayName "Strata 8080" -Direction Inbound -Protocol TCP -LocalPort 8080 -Action Allow -Profile Private`
+  in an admin PowerShell, and make sure the network is set to Private.
+- **From the internet.** Put a tunnel in front of it, for example [cloudflared](https://developers.cloudflare.com/cloudflare-one/connections/connect-networks/do-more-with-tunnels/trycloudflare/):
  `cloudflared tunnel --url http://127.0.0.1:8080`. **Set a key first**, or anyone with the link can use your PC:
  add `"api_key": "some-long-secret"` to `strata-<model>.json` (or set the `STRATA_API_KEY` environment variable);
  clients then send it as their API key.
@@ -301,6 +324,45 @@ test images.

 ---

+## Experimental speed projection (EXPERIMENTAL, off by default)
+
+**This is an experiment, not a finished feature.** It ships with Strata but stays off unless you turn it on.
+
+A 480 KB control vector for Qwen3.8-Flash-Next (`data/experimental-speed-projection/`, see its README). After each
+of layers 4-44 the engine removes one direction from every hyper-connection stream of the residual: `h -= (h . v) v`,
+one unit vector `v` per layer, exactly as llama.cpp does with the package's `--cvec-mode project` patches.
+
+**What it changes.** The vector's own package describes it as a **refusal-direction projection**: with it the model
+declines far fewer requests (it reports 1 of 50 vs 50 of 50 on its test set), and removing refusals removes a safety
+behaviour - you are responsible for what the model writes with it on. It also shifts ordinary answers a little
+(measured below). It is not an optimization in the engine: on the same text it costs 0.2-0.4% per token. What a
+chat's tokens/s does with it on depends on the text the model writes (length, repetition, how well the drafts land),
+so measure it on your own prompts; the Monitor marks every request ESP or stock.
+
+**Turning it on (at setup).** `START-HERE.bat --setup` asks "Turn on the experimental speed projection?" (default:
+no), or pass `--experimental-speed-projection on` (`off`, or a path to another vector GGUF). Only for the original
+Qwen3.8-Flash-Next, not Swift 1.5. It writes these engine flags (llama.cpp's) into `strata-<model>.json`:
+
+```
+--control-vector-scaled <Strata>\data\experimental-speed-projection\Qwen3.8-Flash-Next-experimental-speed-projection.gguf:1.0
+--control-vector-layer-range 4 44 --cvec-mode project --cvec-dir per-layer
+```
+
+The engine log then says `control vector mode = project, dir = per-layer, layers 4..44 (41 steered)`, and the web
+app's About tab lists it. (`--cvec-mode add` is llama.cpp's stock additive mode, for additive vectors.)
+
+**Per request.** A loaded vector is on for every request unless it says otherwise: the web app's Sampling drawer has
+a switch, and the API takes `"experimental_speed_projection": false` in the request body (OpenAI and Anthropic; a
+config default goes in `"sampling": {"experimental_speed_projection": false}`). Switching drops the conversation
+cache once, since the model state was computed the other way. Switched off, the output is token-for-token the stock
+model's.
+
+**Measured here** (Q2_0, fixed experts, 2,557 teacher-forced tokens of code, a document and a chat): the top-1 token
+changes at 10% of positions, mean KL from the stock model 0.063 nats (max 4.1), perplexity +15% on code, +2.3% on
+the document, +0.4% on the chat. Details: `bench/results/2026-09-27-esp/`.
+
+---
+
 ## Troubleshooting

 | Symptom | What to do |
@@ -309,14 +371,16 @@ test images.
 | Python or the build tools could not be installed | Install what it names (links are printed), then run it again. Everything already done is kept. |
 | `port 8080 is already in use` | Strata is already running (look for its window), or another program uses the port: `START-HERE.bat --port 8081`. |
 | `cudaHostRegister ... out of memory` in the log | Normal on Windows: the engine pins the experts in per-layer slices instead. Only a problem if the load then fails. |
-| The first start takes minutes | It is reading 34-43 GB into RAM; the second start is faster while the files are in the OS cache. |
+| The first start takes minutes | It is reading 34-55 GB into RAM; the second start is faster while the files are in the OS cache. |
+| The PC freezes for a few minutes at the first start | Normal the first time: the engine loads the experts into RAM, pins part of it for the GPU and sizes the expert cache. Wait; don't close the window. Still frozen after 10 minutes: restart the PC, close other programs, try again, or pick a smaller size. |
+| `the engine stopped unexpectedly (exit code ...)` | The engine process ended mid-answer - usually out of RAM (Linux ends the biggest program: `sudo dmesg \| grep -i -E 'killed process\|out of memory'`). The next request starts it again by itself. If it repeats: close other programs or pick a smaller size. The server also warns at start when the model's experts leave less than ~6 GB of RAM for everything else. |
 | Slow output, disk light busy | Not enough free RAM: close other programs, or choose Q2_0 / IQ2_XS. |
 | `prompt ... exceeds the context` | The request is longer than the context you chose: run setup again with a bigger `--context`. |
 | Slower than the tables | The monitor plugged into the GPU and other GPU programs take VRAM from the expert cache; RAM running below its rated speed (enable EXPO/XMP in the BIOS) slows the CPU half. |
 | `this server was started without the vision encoder` | The model was set up for text only: run setup again with `--vision gpu`. |
 | A picture is refused or `cannot read the image` | The file is not a picture Pillow can open (JPEG, PNG, WebP, GIF, BMP, TIFF, AVIF work). |
 | Pictures are slow (10-30 s) | The encoder runs on the CPU: run setup again with `--vision gpu` (needs ~1.4 GB of VRAM). |
-| A request never finishes (older engines, mostly with pictures) | Run `START-HERE.bat` once to get engine 0.1.2 or newer. It keeps a real margin of VRAM free: the log says `... MiB of VRAM free with everything loaded` and warns when it is close to 0. |
+| A request never finishes: "reading the prompt", GPU "100%" at low power | The GPU ran out of VRAM (engines before 0.1.9 could end with ~30 MiB free at large contexts). Run `START-HERE.bat` once to get engine 0.1.9 or newer; the log then says `... MiB of VRAM free with everything loaded` (a few hundred) and names the `--vram-reserve-mib` to add if it is low. |
 | Anything else | The engine log is `strata-<model>.log` in this folder. |

 ---
@@ -353,3 +417,5 @@ The full story, with measurements, bottlenecks and what comes next: **[docs/pape
  `third_party/ggml/LICENSE`.
 - Ideas from [Splash](https://github.com/incoai/splash), [ninfer](https://github.com/Neroued/ninfer) and
  [HyperQwen](https://github.com/syv-ai/HyperQwen); references in the paper.
+- The web app's font: [Outfit](https://github.com/Outfitio/Outfit-Fonts) (SIL Open Font License 1.1, see
+  `serve/web/fonts/OFL.txt`). Its Monitor tab started from @code-martin's dashboard idea (PR #22).
@@ -207,6 +207,10 @@ struct QsaState {
    int8_t* v_q = nullptr;
    uint16_t* k_scale = nullptr;
    uint16_t* v_scale = nullptr;
+    /// PR #21: Q4_0 KV with Walsh-Hadamard rotation (qsa_set_kv_q4, kv_q4.hpp): 144 B per cell and head
+    bool kv_q4 = false;
+    uint8_t* k_q4 = nullptr;
+    uint8_t* v_q4 = nullptr;
    int32_t* page_table = nullptr;   ///< (n_pages,) logical page -> physical page (-1: not resident, streamed)
    int64_t n_pages = 0;
    int64_t max_cells = 0;
@@ -269,6 +273,13 @@ uint64_t qsa_kv_host_bytes();
 /// initializing the session; default off until gate G-C accepts it.
 void qsa_set_kv_int8(bool enabled);
 bool qsa_kv_int8();
+/// PR #21: store K/V as Q4_0 after a Hadamard rotation (`--kv q4_0`): 576 B per cell, vs 1,056 in INT8.
+void qsa_set_kv_q4(bool enabled);
+bool qsa_kv_q4();
+/// The state's KV format for the block-moving functions of kv_stream.hpp (kKvF16 / kKvInt8 / kKvQ4).
+inline int qsa_kv_format(const QsaState& st) {
+    return st.kv_q4 ? strata::kernels::kKvQ4 : st.kv_int8 ? strata::kernels::kKvInt8 : strata::kernels::kKvF16;
+}
 uint64_t qsa_state_init(const ModelGeometry& g, int64_t max_cells, void* base, QsaState& st,
                        const QsaState* share_rope = nullptr, int64_t ring_cells = 0);
 /// KV streaming: the pools a reader sees (the VRAM slots) and, when streamed, make the selection's blocks resident.
@@ -0,0 +1,56 @@
+// include/strata/kernels/cvec.hpp - a control vector on the residual stream: the engine's side of llama.cpp's
+// `llama_adapter_cvec` with the `--cvec-mode` / `--cvec-dir` extension of the `experimental-speed-projection`
+// package (its `02-cvec-projection-mode.patch`, and `01-qwen4exp-cvec-hooks.patch` for WHERE it applies).
+//
+// After layer l's FFN write - llama.cpp's `build_cvec(res_hc, il)` right after the layer's second
+// `build_hc_combine` - every one of the hc residual streams h of every token becomes
+//
+//   project:  h <- h - s_l (h . v_l) v_l        v_l = d / |d|, s_l = |d|, d = the scaled direction
+//   add:      h <- h + d                        llama.cpp's stock additive control vector
+//
+// for l in [first, last].  `d` is the GGUF's `direction.<src>` times the file's scale (summed over files), with
+// src = l (per-layer) or one fixed layer (single:L, project mode only), exactly as llama.cpp's loader places it;
+// layer 0 has no direction.  Off unless a vector is loaded (--control-vector-scaled); a loaded one is switched
+// per request through a device flag, so the captured graphs are the same either way.  With the flag off the
+// result is bit-identical to the engine without a vector: the kernel then only applies the pending residual
+// write, with the fused read's own arithmetic.
+#pragma once
+
+#include <cstdint>
+#include <string>
+#include <vector>
+
+namespace strata::kernels {
+
+struct Cvec {
+    const float* dir = nullptr;   ///< device, n_layers * n_embd: project = the unit v_l, add = d_l; zero rows elsewhere
+    const float* s = nullptr;     ///< device, n_layers: project = s_l, add = 1; 0 = the layer is not steered
+    const int* on = nullptr;      ///< device int: 0 = this request runs the stock model
+    int mode = 0;                 ///< 0 = project, 1 = add
+    int first = 0, last = -1;     ///< the steered layers, inclusive
+    int64_t n_embd = 0, hc = 0;
+    bool loaded() const { return dir != nullptr; }
+    /// Layer l's FFN write is followed by the vector (with a real direction there).  Decided at load time, never
+    /// per request: it changes where the residual write happens, so the graphs depend on it.
+    bool covers(int64_t l) const { return loaded() && l >= first && l <= last && l < (int64_t) steered.size() && steered[(size_t) l]; }
+    std::vector<bool> steered;    ///< host copy of s_l != 0
+};
+
+/// The loaded vector (empty until `cvec_upload`).
+const Cvec& cvec();
+
+/// Upload a vector built by the loader: `dir` is n_layers * n_embd, `s` n_layers (see `Cvec`).  Starts ON.
+bool cvec_upload(const std::vector<float>& dir, const std::vector<float>& s, int mode, int first, int last,
+                 int64_t n_embd, int64_t hc, std::string& err);
+
+/// The per-request switch.  Synchronizes the device when it changes, so call it between requests.
+void cvec_set_enabled(bool on);
+bool cvec_enabled();
+
+/// Layer `layer`'s vector on T tokens' residual stacks (`R + t * r_ld`, hc streams of n_embd).  With `write`, the
+/// pending FFN write `R += bo * 2 sigmoid(inj / hc)` (the fused read's arithmetic) is applied first, for callers
+/// whose writes are folded into the next layer's read; without it, R must already hold the layer's output.
+void cvec_apply(float* R, int64_t layer, int64_t T, int64_t r_ld, const float* bo, int64_t bo_ld, const float* inj,
+                int64_t inj_ld, bool write, void* stream);
+
+}  // namespace strata::kernels
@@ -0,0 +1,61 @@
+// include/strata/kernels/kv_q4.hpp - Q4_0 KV storage with Walsh-Hadamard rotation for the QSA layers (`--kv q4_0`).
+//
+// From PR #21 (code-martin). K and V are rotated by the orthonormal 256-point Hadamard matrix H before they are
+// quantized to ggml's q4_0 (32 values per block: an fp16 scale + 16 bytes of 4-bit codes, 144 B per head per cell,
+// 576 B per cell for K and V of both heads, vs 1,056 B in INT8). The rotation spreads a head's outlier channels
+// over all 256 dimensions, which is what makes 4 bits usable. The query is rotated the same way, so
+// <Hq, Hk> = <q, k> and the scores are unchanged, and the attention output (a mix of rotated values) is rotated
+// back with H (self-inverse). What is lossy is the 4-bit rounding itself: measured, not assumed, in
+// bench/results/2026-09-27-kv-q4.
+//
+// With KV streaming (--kv-resident) the codes live in the host copy and the resident slots like INT8's (KvHostPools
+// k_q4/v_q4, the same block granule), so both options combine.
+#pragma once
+
+#include "strata/kernels/qsa.hpp"
+
+#include <cstdint>
+
+namespace strata::kernels {
+
+inline constexpr int QK4_0 = 32;
+
+#pragma pack(push, 1)
+struct block_q4_0 {
+    uint16_t d;             // scale (fp16 bits)
+    uint8_t qs[QK4_0 / 2];  // 32 4-bit codes: element j in the low nibble of qs[j], j + 16 in the high one
+};
+#pragma pack(pop)
+
+static_assert(sizeof(block_q4_0) == 18, "block_q4_0 must be 18 bytes");
+
+/// Bytes per cell and KV head: 8 blocks of 32 for head_dim 256 -> 144.
+inline uint64_t kv_q4_bytes_per_head(int head_dim) { return (uint64_t) (head_dim / QK4_0) * sizeof(block_q4_0); }
+
+/// Bytes per cell (one token, one layer): K and V of every KV head.
+inline uint64_t kv_q4_bytes_per_cell(const QsaShapes& s) {
+    return (uint64_t) s.n_head_kv * kv_q4_bytes_per_head((int) s.head_dim) * 2;
+}
+
+/// Orthonormal Fast Walsh-Hadamard Transform of rows of 256 floats (scale 1/16): its own inverse.
+void fwht256_cuda(const float* src, float* dst, int64_t n_rows, void* stream);
+inline void fwht256_inplace_cuda(float* data, int64_t n_rows, void* stream) { fwht256_cuda(data, data, n_rows, stream); }
+
+/// Append the (already rotated) cell at step[kStepPos]. With a host copy (KV streaming) it is written there too,
+/// and to VRAM only if its block is resident.
+void kv_append_q4_step(uint8_t* k_q4, uint8_t* v_q4, const int32_t* page_table, const int32_t* step,
+                       const float* kcur, const float* vcur, const QsaShapes& s, void* stream,
+                       const KvHostPools* host = nullptr);
+
+/// The prompt path: T consecutive (rotated) cells from pos0, K/V [T, n_head_kv, 256]; also into `stage` (identity
+/// layout, the one-layer staging pool of a streamed session) when given.
+void kv_append_q4(uint8_t* k_q4, uint8_t* v_q4, const int32_t* page_table, int64_t pos0, int64_t T, const float* K,
+                  const float* V, const QsaShapes& s, void* stream, const KvHostPools* host = nullptr,
+                  const KvHostPools* stage = nullptr);
+
+/// Gather step[kStepWidth] cells named by `ids` into FP16 scratch [id][kv_head][head_dim] (still rotated).
+void kv_gather_q4_step(const uint8_t* k_q4, const uint8_t* v_q4, const int32_t* page_table, const int32_t* ids,
+                       const int32_t* step, int64_t max_ids, const QsaShapes& s, uint16_t* k_scratch,
+                       uint16_t* v_scratch, void* stream);
+
+}  // namespace strata::kernels
@@ -36,9 +36,15 @@ struct KvHostPools {
    int8_t* v_q = nullptr;
    uint16_t* k_scale = nullptr;  ///< int8 mode: fp16 scale per 64 values
    uint16_t* v_scale = nullptr;
-    bool present() const { return k_pool != nullptr || k_q != nullptr; }
+    uint8_t* k_q4 = nullptr;      ///< q4_0 mode (kv_q4.hpp): block_q4_0 codes, 144 B per cell and head
+    uint8_t* v_q4 = nullptr;
+    bool present() const { return k_pool != nullptr || k_q != nullptr || k_q4 != nullptr; }
 };

+/// The KV storage format, for the functions below that move whole blocks (`fmt`): fp16, int8 (+ scales), q4_0.
+/// (A bool `int8` argument still reads as kKvF16 / kKvInt8.)
+enum KvFormat : int { kKvF16 = 0, kKvInt8 = 1, kKvQ4 = 2 };
+
 /// The residency map of a streamed layer, all device memory at fixed addresses (the graphs bake them in).
 struct KvStreamMap {
    int32_t* page_table = nullptr;  ///< (n_blocks,) block -> slot, or -1
@@ -62,7 +68,7 @@ void kv_stream_reset(const KvStreamMap& m, void* stream);
 /// Make every block named by the selections of `n_q` queries resident (ids [n_q][cap], width from
 /// steps[q * kStepCount + kStepWidth]). Capturable. `n_slots` must hold the distinct blocks of one call
 /// (n_q x (cap / page_size + 2)); a call that cannot sets ctl[3] (see `kv_stream_counters`).
-void kv_stream_resolve(const KvStreamMap& m, const QsaAttnPools& slots, const KvHostPools& host, bool int8,
+void kv_stream_resolve(const KvStreamMap& m, const QsaAttnPools& slots, const KvHostPools& host, int fmt,
                       const int32_t* ids, const int32_t* steps, int64_t n_q, int64_t cap, const QsaShapes& s,
                       void* stream);

@@ -71,12 +77,12 @@ void kv_ring_table(int32_t* page_table, int64_t n_blocks, int64_t n_slots, void*

 /// Copy blocks [b0, b1) of the host copy into the pool pages `page_table` names (host-side table `phys(b)`
 /// computed as `b % n_slots`: the ring restore). Not capturable.
-void kv_ring_restore(const QsaAttnPools& slots, const KvHostPools& host, bool int8, int64_t b0, int64_t b1,
+void kv_ring_restore(const QsaAttnPools& slots, const KvHostPools& host, int fmt, int64_t b0, int64_t b1,
                     int64_t n_slots, const QsaShapes& s, void* stream);

 /// Copy the first `n_blocks` blocks of the host copy into a fully resident (identity-layout) pool: the prompt
 /// path's staging of one layer. Not capturable (DMA).
-void kv_stage_from_host(const QsaAttnPools& stage, const KvHostPools& host, bool int8, int64_t n_blocks,
+void kv_stage_from_host(const QsaAttnPools& stage, const KvHostPools& host, int fmt, int64_t n_blocks,
                        const QsaShapes& s, void* stream);

 struct KvStreamCounters {
@@ -87,6 +93,6 @@ struct KvStreamCounters {
 KvStreamCounters kv_stream_counters(const KvStreamMap& m);

 /// Bytes of one block (page) of K and V together.
-uint64_t kv_block_bytes(const QsaShapes& s, bool int8);
+uint64_t kv_block_bytes(const QsaShapes& s, int fmt);

 }  // namespace strata::kernels
@@ -27,6 +27,8 @@ struct QsaAttnPools {
    const int8_t* v_q = nullptr;
    const uint16_t* k_scale = nullptr;  ///< fp16 [page][kv_head][page_size][head_dim / 64]
    const uint16_t* v_scale = nullptr;
+    const uint8_t* k_q4 = nullptr;      ///< q4_0 block_q4_0 [page][kv_head][page_size][head_dim / 32 * 18]
+    const uint8_t* v_q4 = nullptr;
    const int32_t* page_table = nullptr;
 };

@@ -1,152 +0,0 @@
-<!doctype html>
-<html lang="en">
-<head>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<title>Strata</title>
-<style>
-  :root { --bg:#f6f7f9; --card:#fff; --fg:#1b1f24; --mute:#667085; --line:#e3e6ea; --acc:#2f6fed; --me:#e9f0ff; --ok:#1a7f37; }
-  @media (prefers-color-scheme: dark) {
-    :root { --bg:#111418; --card:#1a1e24; --fg:#e7e9ec; --mute:#9aa3ae; --line:#2b313a; --acc:#6b9bff; --me:#1f2b44; --ok:#3fb950; }
-  }
-  * { box-sizing: border-box; }
-  body { margin:0; background:var(--bg); color:var(--fg); font:15px/1.5 system-ui, -apple-system, "Segoe UI", sans-serif; }
-  main { max-width:860px; margin:0 auto; padding:16px; display:flex; flex-direction:column; gap:12px; min-height:100vh; }
-  .card { background:var(--card); border:1px solid var(--line); border-radius:12px; padding:14px 16px; }
-  h1 { font-size:20px; margin:0; display:flex; align-items:center; gap:8px; }
-  .dot { width:10px; height:10px; border-radius:50%; background:var(--mute); display:inline-block; }
-  .dot.on { background:var(--ok); }
-  .mute { color:var(--mute); font-size:13px; }
-  code { background:var(--bg); border:1px solid var(--line); border-radius:6px; padding:1px 6px; font-size:13px; }
-  .grid { display:grid; grid-template-columns: max-content 1fr; gap:4px 12px; margin-top:8px; font-size:14px; }
-  #log { flex:1; display:flex; flex-direction:column; gap:10px; overflow-y:auto; min-height:240px; }
-  .msg { padding:10px 12px; border-radius:10px; white-space:pre-wrap; word-wrap:break-word; }
-  .msg.user { background:var(--me); align-self:flex-end; max-width:85%; }
-  .msg.bot { background:var(--card); border:1px solid var(--line); }
-  .msg img { max-width:220px; max-height:160px; display:block; border-radius:6px; margin-bottom:6px; }
-  details { color:var(--mute); font-size:13px; margin-bottom:6px; }
-  form { display:flex; gap:8px; align-items:flex-end; flex-wrap:wrap; }
-  textarea { flex:1; min-width:200px; min-height:52px; max-height:200px; resize:vertical; padding:10px; border-radius:10px;
-             border:1px solid var(--line); background:var(--card); color:var(--fg); font:inherit; }
-  button, label.btn { border:1px solid var(--line); background:var(--card); color:var(--fg); border-radius:10px; padding:10px 14px;
-                      font:inherit; cursor:pointer; }
-  button.primary { background:var(--acc); border-color:var(--acc); color:#fff; }
-  button:disabled { opacity:.5; cursor:default; }
-  .row { display:flex; gap:14px; align-items:center; flex-wrap:wrap; font-size:13px; color:var(--mute); }
-  #att { font-size:13px; color:var(--mute); }
-</style>
-</head>
-<body>
-<main>
-  <div class="card">
-    <h1><span id="dot" class="dot"></span> Strata <span id="state" class="mute">connecting ...</span></h1>
-    <div class="grid">
-      <span class="mute">Model</span><span id="model">-</span>
-      <span class="mute">Context</span><span id="ctx">-</span>
-      <span class="mute">Images</span><span id="img">-</span>
-      <span class="mute">OpenAI API</span><span><code id="oai"></code> (any API key<span id="keynote"></span>)</span>
-      <span class="mute">Anthropic API</span><span><code id="ant"></code></span>
-    </div>
-    <p class="mute" style="margin:10px 0 0">Point any app with an "OpenAI-compatible" or "Anthropic" provider at these
-      addresses. Close the Strata window to stop the model.</p>
-  </div>
-
-  <div id="log" class="card"></div>
-
-  <div class="card">
-    <form id="f">
-      <textarea id="q" placeholder="Ask something ... (Enter sends, Shift+Enter = new line)"></textarea>
-      <label class="btn" id="attbtn" hidden>Picture<input id="file" type="file" accept="image/png,image/jpeg,image/gif,image/bmp" hidden></label>
-      <button class="primary" id="send">Send</button>
-      <button type="button" id="stop" disabled>Stop</button>
-    </form>
-    <div class="row" style="margin-top:8px">
-      <label>Thinking <select id="think" style="padding:4px;border-radius:8px;border:1px solid var(--line);background:var(--card);color:var(--fg)">
-        <option value="none">off - answer at once</option>
-        <option value="low">low</option>
-        <option value="medium" selected>medium</option>
-        <option value="high">high - best on hard questions, slowest</option>
-      </select></label>
-      <span id="att"></span>
-      <button type="button" id="clear" style="padding:4px 10px">New chat</button>
-      <input id="key" type="password" placeholder="API key" hidden style="padding:6px;border-radius:8px;border:1px solid var(--line)">
-    </div>
-  </div>
-</main>
-<script>
-const $ = id => document.getElementById(id);
-const base = location.origin;
-$("oai").textContent = base + "/v1";
-$("ant").textContent = base + "/v1/messages";
-let history = [], image = null, ctrl = null;
-
-async function status() {
-  try {
-    const h = await (await fetch("/health")).json();
-    $("dot").classList.add("on"); $("state").textContent = "running";
-    $("model").textContent = h.model; $("ctx").textContent = h.max_context.toLocaleString() + " tokens";
-    $("img").textContent = h.images ? "on" : "off (run setup again and answer yes to images)";
-    $("attbtn").hidden = !h.images;
-    if (h.api_key) { $("key").hidden = false; $("keynote").textContent = " - this server needs its key"; }
-  } catch (e) { $("state").textContent = "not reachable - is the Strata window still open?"; }
-}
-status();
-
-function bubble(cls, text) {
-  const d = document.createElement("div"); d.className = "msg " + cls; d.textContent = text || "";
-  $("log").appendChild(d); $("log").scrollTop = $("log").scrollHeight; return d;
-}
-
-$("file").onchange = () => {
-  const f = $("file").files[0]; if (!f) return;
-  const r = new FileReader();
-  r.onload = () => { image = r.result; $("att").textContent = "picture: " + f.name; };
-  r.readAsDataURL(f);
-};
-$("clear").onclick = () => { history = []; $("log").innerHTML = ""; };
-$("stop").onclick = () => ctrl && ctrl.abort();
-$("q").addEventListener("keydown", e => { if (e.key === "Enter" && !e.shiftKey) { e.preventDefault(); $("f").requestSubmit(); } });
-
-$("f").onsubmit = async e => {
-  e.preventDefault();
-  const text = $("q").value.trim(); if (!text) return;
-  $("q").value = "";
-  const content = image ? [{type: "image_url", image_url: {url: image}}, {type: "text", text}] : text;
-  const u = bubble("user", "");
-  if (image) { const im = document.createElement("img"); im.src = image; u.appendChild(im); }
-  u.appendChild(document.createTextNode(text));
-  history.push({role: "user", content});
-  image = null; $("att").textContent = ""; $("file").value = "";
-  const b = bubble("bot", ""); const det = document.createElement("details"); const sum = document.createElement("summary");
-  sum.textContent = "thinking"; det.appendChild(sum); const th = document.createElement("div"); det.appendChild(th);
-  const ans = document.createElement("div"); b.appendChild(ans);
-  $("send").disabled = true; $("stop").disabled = false; ctrl = new AbortController();
-  let answer = "";
-  try {
-    const headers = {"Content-Type": "application/json"};
-    if ($("key").value) headers["Authorization"] = "Bearer " + $("key").value;
-    const body = {model: "strata", messages: history, stream: true, max_tokens: 8192};
-    body.reasoning_effort = $("think").value;
-    const r = await fetch("/v1/chat/completions", {method: "POST", headers, body: JSON.stringify(body), signal: ctrl.signal});
-    if (!r.ok) { ans.textContent = "error: " + ((await r.json()).error || {}).message; throw 0; }
-    const rd = r.body.getReader(), dec = new TextDecoder(); let buf = "";
-    for (;;) {
-      const {value, done} = await rd.read(); if (done) break;
-      buf += dec.decode(value, {stream: true});
-      let i;
-      while ((i = buf.indexOf("\n\n")) >= 0) {
-        const line = buf.slice(0, i).trim(); buf = buf.slice(i + 2);
-        if (!line.startsWith("data: ") || line === "data: [DONE]") continue;
-        const d = JSON.parse(line.slice(6)).choices[0].delta;
-        if (d.reasoning_content) { if (!det.parentNode) b.insertBefore(det, ans); th.textContent += d.reasoning_content; }
-        if (d.content) { answer += d.content; ans.textContent = answer; }
-        $("log").scrollTop = $("log").scrollHeight;
-      }
-    }
-  } catch (err) { if (err && err.name === "AbortError") ans.textContent = answer + " [stopped]"; }
-  history.push({role: "assistant", content: answer});
-  $("send").disabled = false; $("stop").disabled = true; ctrl = null;
-};
-</script>
-</body>
-</html>
@@ -19,6 +19,7 @@ talks to it over stdin/stdout; `MockEngine` is a scripted stand-in that makes ev
 from __future__ import annotations

 import argparse
+import collections
 import base64
 import hashlib
 import json
@@ -71,6 +72,10 @@ class MockEngine:
            yield t


+class EngineDied(RuntimeError):
+    """The engine process ended in the middle of a request (issue #27: on Linux, the out-of-memory killer)."""
+
+
 class StrataEngine:
    """The resident engine: `strata --serve` reads `GEN <max_new> <ids>` lines and streams `T <id>` lines, then
    `DONE ...`.  Requests are serialized by the service's FIFO, so one pipe is enough.
@@ -82,13 +87,25 @@ class StrataEngine:

    def __init__(self, exe: str, args: list[str], cwd: str | None = None, log: str | None = None,
                 env: dict | None = None):
+        self.spawn = (exe, list(args), cwd, log, env)   # to start it again after it died (issue #27)
+        self.log_path = log
        self.log = open(log, "a", encoding="utf-8") if log else subprocess.DEVNULL
        self.proc = subprocess.Popen([exe, "--serve", *args], cwd=cwd, stdin=subprocess.PIPE, stdout=subprocess.PIPE,
                                     stderr=self.log, text=True, encoding="utf-8", bufsize=1, env=env)
        self.max_context = 0
        self.can_stop = False            # the engine honours a STOP line mid-request (READY <ctx> stop)
        self.last = {}
+        self.info = {}                   # INFO key=value facts (engine 0.1.8+): kv, expert slots, ... (Monitor tab)
+        self.progress = None             # (read, total) prompt tokens while a prompt is read, from PP lines
+        try:                             # a ready-made engine's BUILD.json says its version
+            self.info["version"] = json.loads((Path(exe).parent / "BUILD.json").read_text()).get("version")
+        except (OSError, ValueError):
+            self.info["version"] = None
        for line in self.proc.stdout:
+            if line.startswith("INFO "):
+                for kv in line.split()[1:]:
+                    k, _, v = kv.partition("=")
+                    self.info[k] = int(v) if v.lstrip("-").isdigit() else v
            if line.startswith("READY"):
                f = line.split()
                self.max_context = int(f[1])
@@ -103,12 +120,35 @@ class StrataEngine:
    def _pump(self):
        for line in self.proc.stdout:
            self.lines.put(line)
+        self.ended = True                               # its output closed: it is gone, even before the OS says so
        self.lines.put(None)

+    def alive(self) -> bool:
+        return not getattr(self, "ended", False) and self.proc.poll() is None
+
+    def exit_code(self):
+        try:
+            return self.proc.wait(timeout=5)
+        except subprocess.TimeoutExpired:
+            return None
+
+    def restart(self):
+        """Start the engine again (the same command) after it died; the new process has its own line queue."""
+        try:
+            self.proc.kill()
+        except OSError:
+            pass
+        info = dict(self.info)
+        self.ended = False
+        self.__init__(*self.spawn)
+        self.info = {**info, **self.info}
+
    def _parse_done(self, line):
        f = line.split()
        self.last = {"generated": int(f[1]), "prompt_tokens": int(f[2]), "prompt_ms": float(f[3]),
                     "decode_ms": float(f[4]), "finish": f[5]}
+        if len(f) >= 9:                                   # the conversation cache's fields (engine 0.1.3+)
+            self.last.update(drafts_accepted=int(f[6]), drafts_offered=int(f[7]), reused=int(f[8]))

    @staticmethod
    def sampling_keys(sampling: dict) -> str:
@@ -147,16 +187,27 @@ class StrataEngine:
        seed = sampling.get("seed")
        if isinstance(seed, int) and seed > 0:
            keys += f" seed={seed}"
-        return keys
+        return keys + StrataEngine.projection_key(sampling)
+
+    @staticmethod
+    def projection_key(sampling: dict) -> str:
+        """`cvec=0|1`: the experimental-speed-projection control vector for this request, when the engine was
+        started with one (--control-vector-scaled; an engine without one ignores the key).  Absent = on."""
+        on = sampling.get("experimental_speed_projection")
+        return f" cvec={int(on)}" if isinstance(on, bool) else ""

    def generate(self, ids, max_new, sampling, cancel, embeddings=None):
        """Yields token ids, and None as a heartbeat every 10 s while the engine is quiet (reading a long prompt):
        the HTTP layer turns it into an SSE comment, which keeps clients' watchdogs calm and notices a client that
        has gone.  A consumer that stops early (or `cancel`) makes the engine STOP, so it does not run to max_new."""
-        head = f"GENI {int(max_new)} {embeddings}" if embeddings else \
+        self.progress = None
+        head = f"GENI {int(max_new)}{self.projection_key(sampling or {})} {embeddings}" if embeddings else \
            f"GEN {int(max_new)}{self.sampling_keys(sampling or {}) if not embeddings else ''}"
-        self.proc.stdin.write(f"{head} {','.join(str(int(t)) for t in ids)}\n")
-        self.proc.stdin.flush()
+        try:
+            self.proc.stdin.write(f"{head} {','.join(str(int(t)) for t in ids)}\n")
+            self.proc.stdin.flush()
+        except OSError:                                  # the pipe is gone: the engine died (not the client)
+            raise EngineDied(f"the engine stopped unexpectedly (exit code {self.exit_code()})") from None
        done = False
        try:
            while True:
@@ -169,12 +220,15 @@ class StrataEngine:
                    continue
                if line is None:
                    done = True
-                    raise RuntimeError("the engine process ended")
+                    raise EngineDied(f"the engine stopped unexpectedly (exit code {self.exit_code()})")
                if line.startswith("T "):
                    if cancel.is_set():
                        return
                    yield int(line[2:])
-                elif line.startswith("PP "):             # prompt progress, one per chunk: also a heartbeat (the
+                elif line.startswith("PP "):
+                    f = line.split()
+                    if len(f) >= 3 and f[1].isdigit() and f[2].isdigit():
+                        self.progress = (int(f[1]), int(f[2]))             # prompt progress, one per chunk: also a heartbeat (the
                    if cancel.is_set():                   # lines reset the 10 s wait, so without this a long prompt
                        return                            # would send no keep-alives at all)
                    yield None
@@ -358,17 +412,98 @@ class Detokenizer:

 class Service:
    def __init__(self, engine: Engine, tokenizer, template: ChatTemplate, model_name: str = "qwen3.8-flash-next",
-                 vision: Vision | None = None, sampling_defaults: dict | None = None):
+                 vision: Vision | None = None, sampling_defaults: dict | None = None,
+                 fit_max_tokens: bool = False):
        self.engine, self.tok, self.template, self.model, self.vision = engine, tokenizer, template, model_name, vision
+        self.fit_max_tokens = fit_max_tokens          # --fit-max-tokens: clamp the output cap instead of 400
        self.sampling_defaults = dict(sampling_defaults or {})   # the run config's `sampling` block
+        self.shared = {}                              # the web app's Chat settings for every client (POST /settings)
+        self.shared_path = None                       # where they are kept between starts (next to the config)
        self.fifo = threading.Lock()
        self.embeddings = threading.local()           # the current request's image embeddings file (GENI)
        self.api_key = ""                              # when set, /v1/* needs it (Bearer or x-api-key)
        self.status = {"busy": False, "queued": 0}      # GET /status: what the model is doing right now
+        self.history = collections.deque(maxlen=30)     # the last finished requests, newest last (GET /metrics)
        self.status_lock = threading.Lock()
        self.stop_ids = set(tokenizer.encode(IM_END, parse_special=True) +
                            tokenizer.encode("<|endoftext|>", parse_special=True))

+    def set_shared(self, defaults) -> dict:
+        """The Chat settings every client gets for what it leaves out; {} / None = clients use their own again."""
+        self.shared = clean_shared_defaults(defaults)
+        if self.shared_path:
+            try:
+                if self.shared:
+                    Path(self.shared_path).write_text(json.dumps(self.shared, indent=1), encoding="utf-8")
+                else:
+                    Path(self.shared_path).unlink(missing_ok=True)
+            except OSError as e:
+                print(f"[strata] could not save the shared settings: {e}", flush=True)
+        return self.shared
+
+    def with_shared(self, req: dict, api: str) -> dict:
+        """The request with the shared thinking level and max tokens filled in where it has none of its own."""
+        s = self.shared
+        if not s:
+            return req
+        req = dict(req)
+        if "max_tokens" in s and not req.get("max_tokens") and not req.get("max_completion_tokens"):
+            req["max_tokens"] = s["max_tokens"]
+        effort = s.get("reasoning_effort")
+        if effort:
+            if api == "openai":
+                ctk = req.get("chat_template_kwargs") if isinstance(req.get("chat_template_kwargs"), dict) else {}
+                if not req.get("reasoning_effort") and not req.get("reasoning") and \
+                        "enable_thinking" not in ctk and "reasoning_effort" not in ctk:
+                    req["reasoning_effort"] = effort
+            elif not req.get("thinking") and not req.get("output_config"):
+                if effort == "none":
+                    req["thinking"] = {"type": "disabled"}
+                else:
+                    req["output_config"] = {"effort": effort}
+        return req
+
+    def start_telemetry(self):
+        """The hardware sampler behind GET /metrics (serve/telemetry.py), recording this server's tok/s too."""
+        if getattr(self, "telemetry", None) is None:
+            from serve.telemetry import Telemetry
+            self.telemetry = Telemetry(extra=lambda: {"tok_s": self._tok_s()})
+
+    def _tok_s(self):
+        with self.status_lock:
+            s = dict(self.status)
+        if not s.get("busy") or not s.get("first_token"):
+            return 0.0
+        return s["generated"] / max(1e-6, time.time() - s["first_token"])
+
+    def metrics(self) -> dict:
+        """GET /metrics: what the Monitor tab shows - the engine's facts, what it is doing, the last requests, and
+        the hardware (with a minute of history per series)."""
+        with self.status_lock:
+            s = dict(self.status)
+            hist = list(self.history)
+        now = time.time()
+        progress = getattr(self.engine, "progress", None)
+        if s.get("busy") and s.get("first_token") is None:
+            state = "reading"
+        elif s.get("busy"):
+            state = "generating"
+        else:
+            state = "idle"
+        live = {"state": state, "queued": s.get("queued", 0), "phase": s.get("phase") if s.get("busy") else None,
+                "prompt_tokens": s.get("prompt_tokens") if s.get("busy") else None,
+                "prompt_read": None, "prompt_total": None, "generated": s.get("generated") if s.get("busy") else None,
+                "max_tokens": s.get("max_tokens") if s.get("busy") else None,
+                "elapsed_s": round(now - s["started"], 1) if s.get("busy") and s.get("started") else None,
+                "tok_s": round(self._tok_s(), 1) if state == "generating" else None}
+        if state == "reading" and progress:
+            live["prompt_read"], live["prompt_total"] = progress
+        engine = {"model": self.model, "max_context": self.engine.max_context, "images": self.vision is not None,
+                  **dict(getattr(self.engine, "info", {}) or {})}
+        tel = self.telemetry.snapshot() if getattr(self, "telemetry", None) else {"now": {}, "history": {}, "static": {}}
+        return {"engine": engine, "live": live, "requests": hist[::-1], "hardware": tel["now"], "hardware_static":
+                tel["static"], "history": tel["history"], "time": now}
+
    def prepare(self, messages, tools, kwargs, max_new=None):
        """-> (ids, thinking, max_new). An unset or non-positive max_new (some clients send -1) means "unlimited":
        the rest of the context."""
@@ -403,14 +538,16 @@ class Service:
                    f.write(path.read_bytes())
            self.embeddings.path = combined
        room = self.engine.max_context - CTX_SLACK - len(ids)
-        if max_new is None or max_new <= 0:
+        if max_new is None or max_new <= 0 or (self.fit_max_tokens and room < 1):
            if room < 1:
                raise ValueError(f"prompt ({len(ids)} tokens) leaves no room to answer in the context "
                                 f"({self.engine.max_context}); requests are never truncated")
            max_new = room
        elif max_new > room:
-            raise ValueError(f"prompt ({len(ids)} tokens) + max tokens ({max_new}) exceeds the context "
-                             f"({self.engine.max_context}); requests are never truncated")
+            if not self.fit_max_tokens:
+                raise ValueError(f"prompt ({len(ids)} tokens) + max tokens ({max_new}) exceeds the context "
+                                 f"({self.engine.max_context}); requests are never truncated")
+            max_new = max(1, room)          # --fit-max-tokens: a shorter completion beats a 400
        return ids, kwargs.get("enable_thinking", True) is not False, max_new

    def _note(self, n, evs):
@@ -448,9 +585,10 @@ class Service:

    def run(self, ids, thinking, tools, max_new, sampling, cancel) -> Iterator[tuple[str, object]]:
        """Yields ("event", Event) as text arrives, then ("done", {"finish": .., "completion_tokens": ..})."""
-        if self.sampling_defaults:     # config defaults; the request's own fields win (explicit 0 stays greedy)
+        defaults = {**self.sampling_defaults, **self.shared}   # the config's, then the Chat settings shared with apps
+        if defaults:                   # the request's own fields win (explicit 0 stays greedy)
            req_values = {k: v for k, v in (sampling or {}).items() if v is not None}
-            sampling = {**self.sampling_defaults, **req_values}
+            sampling = {**defaults, **req_values}
        parser = OutputParser(thinking=thinking, tools=tools, stream_tools=True)
        detok, n, finish = Detokenizer(self.tok), 0, "length"
        raw_ids = []                                    # every generated id (STRATA_DEBUG: dump raw model text)
@@ -461,6 +599,14 @@ class Service:
            with self.fifo:
                with self.status_lock:
                    self.status["queued"] -= 1
+                if hasattr(self.engine, "alive") and not self.engine.alive():
+                    # issue #27: it died in an earlier request - start it again instead of failing every request
+                    code = self.engine.exit_code() if hasattr(self.engine, "exit_code") else None
+                    print(f"[strata] the engine had stopped (exit code {code}); starting it again "
+                          "(a minute or two) ...", flush=True)
+                    self.engine.restart()
+                    print("[strata] the engine is running again", flush=True)
+                with self.status_lock:
                    self.status.update(busy=True, phase="reading the prompt", prompt_tokens=len(ids), generated=0,
                                       started=time.time(), first_token=None, tool=None, tail="", max_tokens=max_new)
                last_print = time.time()
@@ -485,6 +631,14 @@ class Service:
                            yield "event", ev
                    if cancel.is_set():
                        finish = "cancel"
+                except EngineDied as e:
+                    finish = "error"
+                    print(f"[strata] {e}. The usual cause is running out of RAM: Linux then ends the biggest program "
+                          "(check: sudo dmesg | grep -i -E 'killed process|out of memory'); Windows slows down instead. "
+                          "Close other programs or use a smaller model (Q2_0 / IQ2_XS). The next request starts the engine "
+                          f"again.{' Its log: ' + self.engine.log_path if getattr(self.engine, 'log_path', None) else ''}",
+                          flush=True)
+                    raise
                finally:
                    gen.close()                         # STOP+drain to THIS request's DONE while still holding the
                    #                                     fifo, so a stop-token break can't leave the shared engine
@@ -497,6 +651,17 @@ class Service:
                Path(emb).unlink(missing_ok=True)
            with self.status_lock:
                if self.status.get("busy"):
+                    last = dict(getattr(self.engine, "last", {}) or {})
+                    started = self.status.get("started", time.time())
+                    loaded = str((getattr(self.engine, "info", {}) or {}).get("cvec", 0)) not in ("0", "", "None")
+                    self.history.append({
+                        "projection": (sampling or {}).get("experimental_speed_projection") is not False
+                        if loaded else None,
+                        "time": started, "duration_s": round(time.time() - started, 1), "finish": finish,
+                        "prompt_tokens": len(ids), "reused": last.get("reused"), "output_tokens": n,
+                        "prompt_ms": last.get("prompt_ms"), "decode_ms": last.get("decode_ms"),
+                        "decode_tok_s": round(last["generated"] / (last["decode_ms"] / 1000), 1)
+                        if n and last.get("generated") and last.get("decode_ms") else None})
                    now = time.time()
                    el = now - self.status.get("started", now)
                    ft = self.status.get("first_token")
@@ -713,8 +878,49 @@ def make_handler(svc: Service):

        def do_GET(self):
            path = self.path.split("?")[0].rstrip("/")
+            if path.startswith("/fonts/"):
+                # the web app's font (Outfit, OFL: serve/web/fonts); the page falls back to the system font
+                name = path[len("/fonts/"):]
+                f = ROOT / "serve" / "web" / "fonts" / name
+                if "/" in name or "\\" in name or not name.endswith(".woff2") or not f.is_file():
+                    self._json(404, {"error": {"message": "not found"}})
+                    return
+                body = f.read_bytes()
+                self.send_response(200)
+                self.send_header("Content-Type", "font/woff2")
+                self.send_header("Cache-Control", "max-age=86400")
+                self.send_header("Content-Length", str(len(body)))
+                self.end_headers()
+                self.wfile.write(body)
+                return
+            if path.startswith("/web/"):
+                # the web app's own files (serve/web): styles, script, icon sprite - same origin, no CDN
+                name = path[len("/web/"):]
+                types = {".css": "text/css; charset=utf-8", ".js": "text/javascript; charset=utf-8",
+                         ".svg": "image/svg+xml"}
+                f = ROOT / "serve" / "web" / name
+                ext = os.path.splitext(name)[1]
+                if "/" in name or "\\" in name or ext not in types or not f.is_file():
+                    self._json(404, {"error": {"message": "not found"}})
+                    return
+                body = f.read_bytes()
+                self.send_response(200)
+                self.send_header("Content-Type", types[ext])
+                self.send_header("Cache-Control", "no-cache")
+                self.send_header("Content-Length", str(len(body)))
+                self.end_headers()
+                self.wfile.write(body)
+                return
+            if path == "/metrics":
+                if self._authorized():
+                    self._json(200, svc.metrics())
+                return
+            if path == "/settings":
+                if self._authorized():
+                    self._json(200, {"shared": bool(svc.shared), "defaults": svc.shared})
+                return
            if path == "":
-                body = (ROOT / "serve" / "index.html").read_bytes()
+                body = (ROOT / "serve" / "web" / "index.html").read_bytes()
                self.send_response(200)
                self.send_header("Content-Type", "text/html; charset=utf-8")
                self.send_header("Content-Length", str(len(body)))
@@ -743,6 +949,9 @@ def make_handler(svc: Service):
        def do_POST(self):
            if not self._authorized():
                return
+            if self.path.rstrip("/") == "/settings":
+                self._settings()
+                return
            try:
                req = json.loads(self.rfile.read(int(self.headers.get("Content-Length", 0))) or b"{}")
                if self.path.rstrip("/") == "/v1/chat/completions":
@@ -753,6 +962,30 @@ def make_handler(svc: Service):
                    self._json(404, {"error": {"message": "not found"}})
            except ValueError as e:
                self._json(400, {"error": {"type": "invalid_request_error", "message": str(e)}})
+            except EngineDied as e:                          # before the answer started (not streamed)
+                self._json(503, {"error": {"type": "server_error", "message": f"{e}; the next request restarts it"}})
+
+        def _settings(self):
+            # They change what every client gets, so only the app's own page may set them: JSON only (a form or a
+            # "simple" cross-site request can't send it without a CORS preflight, which this server never grants),
+            # and no foreign Origin
+            body = self.rfile.read(int(self.headers.get("Content-Length", 0)))
+            if not self.headers.get("Content-Type", "").startswith("application/json"):
+                self._json(415, {"error": {"message": "send application/json"}})
+                return
+            origin = self.headers.get("Origin")
+            if origin and origin.split("://", 1)[-1] != self.headers.get("Host", ""):
+                self._json(403, {"error": {"message": "settings can only be changed from Strata's own page"}})
+                return
+            try:
+                req = json.loads(body or b"{}")
+                shared = svc.set_shared(req.get("defaults") if isinstance(req, dict) else None)
+            except ValueError as e:
+                self._json(400, {"error": {"type": "invalid_request_error", "message": str(e)}})
+                return
+            print("[strata] other apps now use the Chat settings: " + ", ".join(f"{k}={v}" for k, v in shared.items())
+                  if shared else "[strata] other apps use their own settings again", flush=True)
+            self._json(200, {"shared": bool(shared), "defaults": shared})

        def _sse(self):
            self.send_response(200)
@@ -761,6 +994,7 @@ def make_handler(svc: Service):
            self.end_headers()

        def _openai(self, req):
+            req = svc.with_shared(req, "openai")
            messages, tools, kw = openai_to_messages(req)
            max_new = int(req.get("max_completion_tokens") or req.get("max_tokens") or 0)   # 0/-1: the rest of the context
            ids, thinking, max_new = svc.prepare(messages, tools, kw, max_new)
@@ -781,8 +1015,12 @@ def make_handler(svc: Service):
            except OSError:
                cancel.set()                                 # client went away: stop the engine
                chunks.close()
+            except EngineDied as e:                          # mid-stream: say so, then end the stream properly
+                err = {"error": {"type": "server_error", "message": f"{e}; the next request restarts it"}}
+                self.wfile.write(b"data: " + json.dumps(err).encode() + b"\n\ndata: [DONE]\n\n")

        def _anthropic(self, req):
+            req = svc.with_shared(req, "anthropic")
            messages, tools, kw = anthropic_to_messages(req)
            max_new = int(req.get("max_tokens") or 0)                  # 0/-1: the rest of the context
            ids, thinking, max_new = svc.prepare(messages, tools, kw, max_new)
@@ -804,6 +1042,9 @@ def make_handler(svc: Service):
            except OSError:
                cancel.set()
                events.close()
+            except EngineDied as e:                          # mid-stream: Anthropic's error event
+                err = {"type": "error", "error": {"type": "api_error", "message": f"{e}; the next request restarts it"}}
+                self.wfile.write(b"event: error\ndata: " + json.dumps(err).encode() + b"\n\n")

    return Handler

@@ -814,12 +1055,92 @@ class Server(ThreadingHTTPServer):
    allow_reuse_address = os.name != "nt"


+def warn_tight_ram(arena_mib) -> None:
+    """The model's experts live in RAM (INFO arena_mib, engine 0.1.10+).  With less than ~6 GB left beside them for the
+    system, the engine and this server, Linux ends the engine mid-answer when memory runs out (issue #27) and Windows
+    pages to disk; say so at start instead of after a lost answer."""
+    if not isinstance(arena_mib, int) or arena_mib <= 0:
+        return
+    try:
+        import psutil
+        total = psutil.virtual_memory().total
+    except Exception:  # noqa: BLE001 - psutil is optional here
+        return
+    left = total / 2**30 - arena_mib / 1024
+    if left < 6:
+        print(f"[strata] WARNING: RAM is tight - the model's experts take {arena_mib / 1024:.1f} GB of this PC's "
+              f"{total / 2**30:.0f} GB, leaving {left:.1f} GB for everything else. "
+              + ("Linux may stop the engine in the middle of an answer. " if os.name != "nt" else
+                 "Windows will slow down (paging to disk). ")
+              + "Close other programs, or run START-HERE --setup and pick a smaller size (Q2_0 / IQ2_XS).", flush=True)
+
+
+def lan_addresses() -> list[str]:
+    """This PC's IPv4 addresses on its networks (what another device types in), without loopback/link-local."""
+    import socket
+    first, ips = None, set()
+    try:                                                # the address of the default route; sends nothing (UDP)
+        with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
+            s.connect(("10.255.255.255", 1))
+            first = s.getsockname()[0]
+    except OSError:
+        pass
+    try:
+        for info in socket.getaddrinfo(socket.gethostname(), None, socket.AF_INET):
+            ips.add(info[4][0])
+    except OSError:
+        pass
+    ok = lambda ip: ip and not ip.startswith(("127.", "169.254.", "0."))
+    return ([first] if ok(first) else []) + sorted(ip for ip in ips if ok(ip) and ip != first)
+
+
 def serve(svc: Service, host="127.0.0.1", port=8095) -> ThreadingHTTPServer:
+    svc.start_telemetry()
    httpd = Server((host, port), make_handler(svc))
    threading.Thread(target=httpd.serve_forever, daemon=True).start()
    return httpd


+SHARED_KEYS = ("reasoning_effort", "temperature", "top_p", "top_k", "seed", "max_tokens", "experimental_speed_projection")
+
+
+def clean_shared_defaults(d) -> dict:
+    """The Chat settings other apps get (POST /settings): only known keys, each checked; ValueError names a bad one."""
+    if d is None:
+        return {}
+    if not isinstance(d, dict):
+        raise ValueError("defaults must be an object")
+    out = {}
+    for key, value in d.items():
+        if value is None or value == "":
+            continue
+        number = isinstance(value, (int, float)) and not isinstance(value, bool)
+        if key == "reasoning_effort":
+            if value not in ("none", "low", "medium", "high"):
+                raise ValueError("reasoning_effort: none, low, medium or high")
+        elif key == "temperature":
+            if not number or not 0 <= value <= 2:
+                raise ValueError("temperature: 0..2")
+        elif key == "top_p":
+            if not number or not 0 < value <= 1:
+                raise ValueError("top_p: 0 < top_p <= 1")
+        elif key == "top_k":
+            if not number or value != int(value) or not 1 <= value <= 64:
+                raise ValueError("top_k: an integer 1..64")
+            value = int(value)
+        elif key in ("seed", "max_tokens"):
+            if not number or value != int(value) or value <= 0:
+                raise ValueError(f"{key}: a positive integer")
+            value = int(value)
+        elif key == "experimental_speed_projection":
+            if not isinstance(value, bool):
+                raise ValueError("experimental_speed_projection: true or false")
+        else:
+            raise ValueError(f"unknown setting {key!r}")
+        out[key] = float(value) if key in ("temperature", "top_p") else value
+    return out
+
+
 def sampling_defaults_from_config(cfg: dict) -> dict:
    """The run config's optional `sampling` block: defaults for the sampling fields a request leaves out, so
    a plain client gets configured sampling instead of greedy.  Supported: temperature, top_p, top_k, min_p,
@@ -868,6 +1189,11 @@ def sampling_defaults_from_config(cfg: dict) -> dict:
            if not number or value != int(value) or value <= 0:
                raise SystemExit(f"[strata] config sampling.seed={value!r}: expected a positive integer")
            out[key] = int(value)
+        elif key == "experimental_speed_projection":
+            if not isinstance(value, bool):
+                raise SystemExit(f"[strata] config sampling.experimental_speed_projection={value!r}: expected true or "
+                                 "false (the default for requests that leave it out, when the engine has the vector)")
+            out[key] = value
        else:
            print(f"[strata] config sampling.{key}={value!r}: unknown key, ignored", flush=True)
    return out
@@ -878,16 +1204,22 @@ def main() -> int:
    ap.add_argument("--engine", choices=["mock", "strata"], default="mock")
    ap.add_argument("--config", help="strata engine config (JSON: exe, args, cwd, tokenizer, model_name), "
                                     "written by setup.py")
-    ap.add_argument("--host", default="127.0.0.1")
+    ap.add_argument("--host", default=None,
+                    help="the address to listen on: 127.0.0.1 = this PC only (the default), 0.0.0.0 = also other devices "
+                         "on your network (set an API key); also \"host\" in the config")
    ap.add_argument("--script", default="Thinking about it.</think>\n\nHello from the mock engine.")
    ap.add_argument("--port", type=int, default=8095)
    ap.add_argument("--tokenizer", default=str(ROOT / "pack/full/tokenizer"),
                    help="pack tokenizer directory (falls back to a byte tokenizer if absent)")
    ap.add_argument("--open", action="store_true", help="open the local page in the browser once the model is ready")
+    ap.add_argument("--fit-max-tokens", action="store_true",
+                    help="clamp max_tokens to the remaining context instead of rejecting the request "
+                         "(default: reject with 400, like llama.cpp; also \"fit_max_tokens\": true in the config)")
    ap.add_argument("--api-key", default=os.environ.get("STRATA_API_KEY", ""),
                    help="require this key on /v1/* (Authorization: Bearer ... or x-api-key); also $STRATA_API_KEY")
    a = ap.parse_args()
    cfg = json.loads(Path(a.config).read_text(encoding="utf-8-sig")) if a.config else {}   # Notepad adds a BOM
+    a.host = a.host or cfg.get("host") or "127.0.0.1"   # issue #26: the run scripts pass no --host, the config can
    try:                                                # before the minutes of loading: is the port free?
        Server((a.host, a.port), BaseHTTPRequestHandler).server_close()
    except OSError:
@@ -923,19 +1255,46 @@ def main() -> int:
                            env=env)
        print("loading the model (the first start takes a minute or two) ...", flush=True)
        engine = StrataEngine(cfg["exe"], cfg["args"], cwd=cfg.get("cwd"), log=cfg.get("log"), env=env)
+        warn_tight_ram(engine.info.get("arena_mib"))
    else:
        engine, vision, sampling_defaults = MockEngine(tok, a.script), None, {}
    # the model's own chat template (exported with its tokenizer), else the original model's
    tpl = tpath / "chat_template.jinja"
    svc = Service(engine, tok, ChatTemplate(tpl if tpl.exists() else ROOT / "serve/chat_template.jinja"),
                  model_name=cfg.get("model_name", "qwen3.8-flash-next"), vision=vision,
-                  sampling_defaults=sampling_defaults)
+                  sampling_defaults=sampling_defaults,
+                  fit_max_tokens=a.fit_max_tokens or cfg.get("fit_max_tokens") is True)
    svc.api_key = a.api_key or cfg.get("api_key", "")
+    if a.config:                                        # the Chat settings shared with other apps, from last time
+        svc.shared_path = str(Path(a.config).with_suffix("")) + ".shared-settings.json"
+        try:
+            svc.shared = clean_shared_defaults(json.loads(Path(svc.shared_path).read_text(encoding="utf-8")))
+            if svc.shared:
+                print("[strata] other apps use the Chat settings: " +
+                      ", ".join(f"{k}={v}" for k, v in svc.shared.items()), flush=True)
+        except (OSError, ValueError):
+            svc.shared = {}
    httpd = serve(svc, host=a.host, port=a.port)
-    print(f"ready: http://{a.host}:{a.port}/v1  (OpenAI: /v1/chat/completions, Anthropic: /v1/messages, "
+    here = "127.0.0.1" if a.host in ("0.0.0.0", "", "::") else a.host
+    print(f"ready: http://{here}:{a.port}/v1  (OpenAI: /v1/chat/completions, Anthropic: /v1/messages, "
          f"context {engine.max_context} tokens{', images on' if vision else ''}"
          f"{', API key required' if svc.api_key else ''})", flush=True)
-    print(f"       open http://{a.host}:{a.port}/ in a browser to chat; close this window to stop the model", flush=True)
+    print(f"       open http://{here}:{a.port}/ in a browser to chat; close this window to stop the model", flush=True)
+    if a.host not in ("127.0.0.1", "localhost", "::1"):
+        # issue #26: reachable from other devices - say at which address, and what can still block it
+        ips = lan_addresses()
+        for ip in ips:
+            print(f"       from other devices: http://{ip}:{a.port}/   (API: http://{ip}:{a.port}/v1)", flush=True)
+        if not ips:
+            print("       from other devices: http://<this PC's IP address>:" + str(a.port) + "/", flush=True)
+        if not svc.api_key:
+            print("       WARNING: no API key - anyone on your network can use this model. Add \"api_key\": \"...\" "
+                  "to the config (clients send it as their API key; the web page asks for it)", flush=True)
+        if os.name == "nt":
+            print("       nothing arrives? Windows Firewall blocks it until allowed: accept its prompt for Python, or run "
+                  "in an admin PowerShell:\n         New-NetFirewallRule -DisplayName \"Strata " + str(a.port) + "\" "
+                  "-Direction Inbound -Protocol TCP -LocalPort " + str(a.port) + " -Action Allow -Profile Private\n"
+                  "       (and set this network to Private in Windows' network settings)", flush=True)
    if a.open:
        import webbrowser
        webbrowser.open(f"http://{'127.0.0.1' if a.host in ('0.0.0.0', '') else a.host}:{a.port}/")
@@ -0,0 +1,246 @@
+"""serve/telemetry.py - hardware readings for the web app's Monitor tab (idea from PR #22 by code-martin).
+
+A background thread samples once a second and keeps the last 60 readings of each series for the sparklines:
+- GPU: NVIDIA's own NVML library (nvml.dll / libnvidia-ml.so.1, installed with every driver) through ctypes, so no
+  pip package is needed: load, VRAM, temperature, power, PCIe link and throughput.
+- CPU, RAM, disk: `psutil` when it is installed (setup installs it); without it the CPU and RAM readings fall back to
+  the OS (Windows GlobalMemoryStatusEx / GetSystemTimes, Linux /proc) and the disk rate is absent.
+Anything that cannot be read is None; nothing here can stop the server.
+"""
+from __future__ import annotations
+
+import collections
+import ctypes
+import os
+import platform
+import sys
+import threading
+import time
+
+HISTORY = 60
+
+
+# ------------------------------------------------------------------------------------------------ NVML
+class _Nvml:
+    class Util(ctypes.Structure):
+        _fields_ = [("gpu", ctypes.c_uint), ("memory", ctypes.c_uint)]
+
+    class Mem(ctypes.Structure):
+        _fields_ = [("total", ctypes.c_ulonglong), ("free", ctypes.c_ulonglong), ("used", ctypes.c_ulonglong)]
+
+    def __init__(self, index=0):
+        self.lib = self.dev = None
+        names = ["nvml.dll", os.path.join(os.environ.get("ProgramFiles", r"C:\Program Files"),
+                                          "NVIDIA Corporation", "NVSMI", "nvml.dll")] if os.name == "nt" \
+            else ["libnvidia-ml.so.1", "libnvidia-ml.so"]
+        for n in names:
+            try:
+                self.lib = ctypes.CDLL(n)
+                break
+            except OSError:
+                continue
+        if self.lib is None:
+            return
+        try:
+            init = getattr(self.lib, "nvmlInit_v2", None) or self.lib.nvmlInit
+            if init() != 0:
+                self.lib = None
+                return
+            h = ctypes.c_void_p()
+            get = getattr(self.lib, "nvmlDeviceGetHandleByIndex_v2", None) or self.lib.nvmlDeviceGetHandleByIndex
+            if get(ctypes.c_uint(index), ctypes.byref(h)) != 0:
+                self.lib = None
+                return
+            self.dev = h
+        except (AttributeError, OSError):
+            self.lib = None
+
+    def ok(self):
+        return self.lib is not None and self.dev is not None
+
+    def _uint(self, fn, *args):
+        v = ctypes.c_uint()
+        try:
+            return v.value if getattr(self.lib, fn)(self.dev, *args, ctypes.byref(v)) == 0 else None
+        except (AttributeError, OSError):
+            return None
+
+    def name(self):
+        buf = ctypes.create_string_buffer(96)
+        try:
+            if self.lib.nvmlDeviceGetName(self.dev, buf, ctypes.c_uint(96)) == 0:
+                return buf.value.decode(errors="replace")
+        except (AttributeError, OSError):
+            pass
+        return None
+
+    def read(self):
+        out = {}
+        u = self.Util()
+        try:
+            if self.lib.nvmlDeviceGetUtilizationRates(self.dev, ctypes.byref(u)) == 0:
+                out["util"] = u.gpu
+        except (AttributeError, OSError):
+            pass
+        m = self.Mem()
+        try:
+            if self.lib.nvmlDeviceGetMemoryInfo(self.dev, ctypes.byref(m)) == 0:
+                out["mem_used"], out["mem_total"] = m.used, m.total
+        except (AttributeError, OSError):
+            pass
+        out["temp"] = self._uint("nvmlDeviceGetTemperature", ctypes.c_uint(0))          # NVML_TEMPERATURE_GPU
+        mw = self._uint("nvmlDeviceGetPowerUsage")
+        out["power"] = mw / 1000.0 if mw is not None else None
+        lim = self._uint("nvmlDeviceGetEnforcedPowerLimit")
+        out["power_limit"] = lim / 1000.0 if lim is not None else None
+        out["pcie_gen"] = self._uint("nvmlDeviceGetCurrPcieLinkGeneration")        # drops at idle (power saving)
+        out["pcie_gen_max"] = self._uint("nvmlDeviceGetMaxPcieLinkGeneration")
+        out["pcie_width"] = self._uint("nvmlDeviceGetCurrPcieLinkWidth")
+        rx = self._uint("nvmlDeviceGetPcieThroughput", ctypes.c_uint(1))                 # NVML_PCIE_UTIL_RX_BYTES, KB/s
+        tx = self._uint("nvmlDeviceGetPcieThroughput", ctypes.c_uint(0))
+        out["pcie_rx_mb"] = rx / 1024.0 if rx is not None else None
+        out["pcie_tx_mb"] = tx / 1024.0 if tx is not None else None
+        return out
+
+
+# ------------------------------------------------------------------------------------------------ CPU / RAM
+def _cpu_name():
+    if os.name == "nt":
+        try:
+            import winreg
+            k = winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE, r"HARDWARE\DESCRIPTION\System\CentralProcessor\0")
+            return winreg.QueryValueEx(k, "ProcessorNameString")[0].strip()
+        except OSError:
+            pass
+    elif os.path.exists("/proc/cpuinfo"):
+        for line in open("/proc/cpuinfo", encoding="utf-8", errors="replace"):
+            if line.startswith("model name"):
+                return line.split(":", 1)[1].strip()
+    return platform.processor() or None
+
+
+class _CpuRamFallback:
+    """CPU load and RAM without psutil."""
+
+    def __init__(self):
+        self.prev = self._times()
+
+    def _times(self):
+        if os.name == "nt":
+            idle, kern, user = (ctypes.c_ulonglong() for _ in range(3))
+            if ctypes.windll.kernel32.GetSystemTimes(ctypes.byref(idle), ctypes.byref(kern), ctypes.byref(user)):
+                return idle.value, kern.value + user.value           # kernel time includes idle
+            return None
+        try:
+            f = [int(x) for x in open("/proc/stat").readline().split()[1:]]
+            return f[3] + f[4], sum(f)
+        except (OSError, ValueError):
+            return None
+
+    def cpu(self):
+        cur = self._times()
+        prev, self.prev = self.prev, cur
+        if not cur or not prev or cur[1] == prev[1]:
+            return None
+        return max(0.0, min(100.0, 100.0 * (1 - (cur[0] - prev[0]) / (cur[1] - prev[1]))))
+
+    @staticmethod
+    def ram():
+        if os.name == "nt":
+            class MS(ctypes.Structure):
+                _fields_ = [("dwLength", ctypes.c_ulong), ("dwMemoryLoad", ctypes.c_ulong),
+                            ("ullTotalPhys", ctypes.c_ulonglong), ("ullAvailPhys", ctypes.c_ulonglong),
+                            ("ullTotalPageFile", ctypes.c_ulonglong), ("ullAvailPageFile", ctypes.c_ulonglong),
+                            ("ullTotalVirtual", ctypes.c_ulonglong), ("ullAvailVirtual", ctypes.c_ulonglong),
+                            ("ullAvailExtendedVirtual", ctypes.c_ulonglong)]
+            m = MS()
+            m.dwLength = ctypes.sizeof(MS)
+            if ctypes.windll.kernel32.GlobalMemoryStatusEx(ctypes.byref(m)):
+                return m.ullTotalPhys - m.ullAvailPhys, m.ullTotalPhys
+            return None, None
+        try:
+            info = dict(line.split(":", 1) for line in open("/proc/meminfo"))
+            total = int(info["MemTotal"].split()[0]) * 1024
+            avail = int(info["MemAvailable"].split()[0]) * 1024
+            return total - avail, total
+        except (OSError, KeyError, ValueError):
+            return None, None
+
+
+# ------------------------------------------------------------------------------------------------ the sampler
+class Telemetry:
+    def __init__(self, extra=None):
+        """`extra()` -> dict of more series to record each second (the server's tok/s)."""
+        self.extra = extra
+        self.lock = threading.Lock()
+        self.now: dict = {}
+        self.hist = collections.defaultdict(lambda: collections.deque(maxlen=HISTORY))
+        self.gpu = _Nvml()
+        try:
+            import psutil  # noqa: F401
+            self.ps = sys.modules["psutil"]
+        except ImportError:
+            self.ps = None
+        self.fallback = _CpuRamFallback()
+        self.static = {
+            "gpu_name": self.gpu.name() if self.gpu.ok() else None,
+            "cpu_name": _cpu_name(),
+            "cores": (self.ps.cpu_count(logical=False) if self.ps else None) or None,
+            "threads": os.cpu_count(),
+            "psutil": self.ps is not None,
+        }
+        self._disk_prev = None
+        threading.Thread(target=self._loop, daemon=True).start()
+
+    def _disk(self):
+        if not self.ps:
+            return None, None
+        try:
+            c = self.ps.disk_io_counters()
+        except (OSError, RuntimeError):
+            return None, None
+        t = time.time()
+        prev, self._disk_prev = self._disk_prev, (t, c.read_bytes, c.write_bytes)
+        if prev is None or t <= prev[0]:
+            return None, None
+        dt = t - prev[0]
+        return (c.read_bytes - prev[1]) / dt / 2**20, (c.write_bytes - prev[2]) / dt / 2**20
+
+    def sample(self):
+        s = {}
+        if self.gpu.ok():
+            g = self.gpu.read()
+            s.update({f"gpu_{k}": v for k, v in g.items()})
+        if self.ps:
+            try:
+                s["cpu"] = self.ps.cpu_percent(interval=None)
+                vm = self.ps.virtual_memory()
+                s["ram_used"], s["ram_total"] = vm.total - vm.available, vm.total
+            except (OSError, RuntimeError):
+                pass
+        else:
+            s["cpu"] = self.fallback.cpu()
+            s["ram_used"], s["ram_total"] = self.fallback.ram()
+        s["disk_read_mb"], s["disk_write_mb"] = self._disk()
+        if self.extra:
+            try:
+                s.update(self.extra())
+            except Exception:  # noqa: BLE001 - telemetry must never take the server down
+                pass
+        return s
+
+    def _loop(self):
+        while True:
+            s = self.sample()
+            with self.lock:
+                self.now = s
+                for k in ("gpu_util", "gpu_mem_used", "gpu_temp", "gpu_power", "gpu_pcie_rx_mb", "cpu", "ram_used",
+                          "disk_read_mb", "tok_s"):
+                    v = s.get(k)
+                    self.hist[k].append(round(v, 2) if isinstance(v, float) else v)
+            time.sleep(1.0)
+
+    def snapshot(self):
+        with self.lock:
+            return {"now": dict(self.now), "history": {k: list(v) for k, v in self.hist.items()},
+                    "static": dict(self.static)}
@@ -14,7 +14,7 @@ from pathlib import Path

 sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
 from serve.frontend import ChatTemplate  # noqa: E402
-from serve.server import CTX_SLACK, ByteTokenizer, MockEngine, Service, serve  # noqa: E402
+from serve.server import CTX_SLACK, ByteTokenizer, EngineDied, MockEngine, Service, serve  # noqa: E402

 ROOT = Path(__file__).resolve().parents[1]
 CTX = 4096
@@ -126,5 +126,259 @@ class MaxTokens(unittest.TestCase):
            del os.environ["STRATA_DEBUG"]


+class FitMaxTokens(unittest.TestCase):
+    """PR #24: --fit-max-tokens clamps an explicit budget that overshoots the context instead of a 400."""
+
+    @classmethod
+    def setUpClass(cls):
+        tok = ByteTokenizer()
+        cls.engine = RecordingEngine(tok, "</think>\n\n" + ANSWER, max_context=CTX)
+        cls.svc = Service(cls.engine, tok, ChatTemplate(ROOT / "serve/chat_template.jinja"), fit_max_tokens=True)
+        cls.httpd = serve(cls.svc, port=0)
+        cls.base = f"http://127.0.0.1:{cls.httpd.server_address[1]}"
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.httpd.shutdown()
+        cls.httpd.server_close()
+
+    post = MaxTokens.post
+    call = MaxTokens.call
+
+    def test_overshoot_is_clamped_to_the_room(self):
+        for api in ("openai", "anthropic"):
+            with self.subTest(api=api):
+                s, b, pt, ct = self.call(api, max_tokens=CTX)
+                self.assertEqual(s, 200, b)
+                self.assertEqual(self.engine.last_max_new, CTX - CTX_SLACK - pt)
+
+    def test_a_budget_that_fits_is_unchanged(self):
+        s, b, _, ct = self.call("openai", max_tokens=50)
+        self.assertEqual(s, 200, b)
+        self.assertEqual(self.engine.last_max_new, 50)
+
+    def test_no_room_is_still_a_400(self):
+        _, _, pt0, _ = self.call("openai", max_tokens=1)
+        overhead = pt0 - len("hi")
+        s, b, _, _ = self.call("openai", text="y" * (CTX - CTX_SLACK - overhead), max_tokens=100)
+        self.assertEqual(s, 400, b)
+        self.assertIn("no room to answer", b["error"]["message"])
+
+
+class DyingEngine(MockEngine):
+    """Issue #27: an engine that dies after a few tokens of its first answer, and comes back when restarted."""
+
+    def __init__(self, tok, script, max_context):
+        super().__init__(tok, script, max_context=max_context)
+        self.dead, self.restarts, self.die_after = False, 0, 5
+
+    def alive(self):
+        return not self.dead
+
+    def restart(self):
+        self.dead, self.die_after = False, None
+        self.restarts += 1
+
+    def generate(self, ids, max_new, sampling, cancel, embeddings=None):
+        for i, t in enumerate(super().generate(ids, max_new, sampling, cancel, embeddings)):
+            if self.die_after is not None and i == self.die_after:
+                self.dead = True
+                raise EngineDied("the engine stopped unexpectedly (exit code -9)")
+            yield t
+
+
+class EngineDeath(unittest.TestCase):
+    """Issue #27: a dead engine is an error (not "length"), and the next request starts it again."""
+
+    def test_error_then_restart(self):
+        tok = ByteTokenizer()
+        eng = DyingEngine(tok, "</think>\n\n" + ANSWER, max_context=CTX)
+        svc = Service(eng, tok, ChatTemplate(ROOT / "serve/chat_template.jinja"))
+        httpd = serve(svc, port=0)
+        base = f"http://127.0.0.1:{httpd.server_address[1]}"
+        try:
+            def post(body):
+                req = urllib.request.Request(base + "/v1/chat/completions", data=json.dumps(body).encode(),
+                                             headers={"Content-Type": "application/json"})
+                try:
+                    with urllib.request.urlopen(req, timeout=30) as r:
+                        return r.status, r.read().decode()
+                except urllib.error.HTTPError as e:
+                    with e:
+                        return e.code, e.read().decode()
+            msgs = [{"role": "user", "content": "hi"}]
+            code, text = post({"model": "m", "messages": msgs, "max_tokens": 50, "stream": True})
+            self.assertEqual(code, 200)
+            self.assertIn('"error"', text)
+            self.assertIn("stopped unexpectedly", text)
+            self.assertTrue(text.rstrip().endswith("data: [DONE]"))
+            self.assertEqual(svc.metrics()["requests"][0]["finish"], "error")
+            code, text = post({"model": "m", "messages": msgs, "max_tokens": 50})
+            self.assertEqual(code, 200, text)
+            self.assertEqual(eng.restarts, 1)
+            self.assertEqual(json.loads(text)["usage"]["completion_tokens"], 50)
+        finally:
+            httpd.shutdown()
+            httpd.server_close()
+
+
+class SharedSettings(unittest.TestCase):
+    """The web app's "Use for other apps too": POST /settings makes its Chat settings every client's defaults."""
+
+    @classmethod
+    def setUpClass(cls):
+        import tempfile
+
+        class Sampled(RecordingEngine):
+            def generate(self, ids, max_new, sampling, cancel, embeddings=None):
+                self.last_sampling = dict(sampling or {})
+                yield from super().generate(ids, max_new, sampling, cancel, embeddings)
+
+        tok = ByteTokenizer()
+        cls.engine = Sampled(tok, "</think>\n\nhello", max_context=CTX)
+        cls.svc = Service(cls.engine, tok, ChatTemplate(ROOT / "serve/chat_template.jinja"))
+        cls.tmp = tempfile.TemporaryDirectory()
+        cls.svc.shared_path = os.path.join(cls.tmp.name, "strata-x.shared-settings.json")
+        cls.httpd = serve(cls.svc, port=0)
+        cls.base = f"http://127.0.0.1:{cls.httpd.server_address[1]}"
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.httpd.shutdown()
+        cls.httpd.server_close()
+        cls.tmp.cleanup()
+
+    def req(self, path, body, headers=None, raw=None):
+        h = {"Content-Type": "application/json", **(headers or {})}
+        r = urllib.request.Request(self.base + path, data=raw if raw is not None else json.dumps(body).encode(), headers=h)
+        try:
+            with urllib.request.urlopen(r, timeout=30) as resp:
+                return resp.status, json.loads(resp.read())
+        except urllib.error.HTTPError as e:
+            with e:
+                return e.code, json.loads(e.read())
+
+    def chat(self, **extra):
+        return self.req("/v1/chat/completions", {"model": "m", "messages": [{"role": "user", "content": "hi"}], **extra})
+
+    def tearDown(self):
+        self.svc.set_shared(None)
+
+    def test_other_apps_get_the_chat_settings(self):
+        d = {"temperature": 0.3, "top_p": 0.9, "top_k": 10, "seed": 7, "max_tokens": 77,
+             "reasoning_effort": "low", "experimental_speed_projection": False}
+        code, b = self.req("/settings", {"defaults": d})
+        self.assertEqual(code, 200, b)
+        self.assertTrue(b["shared"])
+        self.assertTrue(os.path.exists(self.svc.shared_path))
+        code, _ = self.chat()                                        # a client that sets nothing
+        self.assertEqual(code, 200)
+        got = self.engine.last_sampling
+        for k in ("temperature", "top_p", "top_k", "seed", "experimental_speed_projection"):
+            self.assertEqual(got[k], d[k], k)
+        self.assertEqual(self.engine.last_max_new, 77)
+        code, _ = self.chat(temperature=0.9, max_tokens=5)          # its own values win
+        self.assertEqual(self.engine.last_sampling["temperature"], 0.9)
+        self.assertEqual(self.engine.last_max_new, 5)
+        r = self.svc.with_shared({"messages": []}, "openai")
+        self.assertEqual(r["reasoning_effort"], "low")
+        self.assertEqual(self.svc.with_shared({"reasoning_effort": "high"}, "openai")["reasoning_effort"], "high")
+        self.assertEqual(self.svc.with_shared({}, "anthropic")["output_config"], {"effort": "low"})
+
+    def test_off_again(self):
+        self.req("/settings", {"defaults": {"temperature": 0.3}})
+        code, b = self.req("/settings", {"defaults": None})
+        self.assertEqual((code, b["shared"]), (200, False))
+        self.assertFalse(os.path.exists(self.svc.shared_path))
+        self.chat()
+        self.assertNotIn("temperature", self.engine.last_sampling)
+
+    def test_only_strata_s_own_page_may_set_them(self):
+        code, _ = self.req("/settings", None, {"Content-Type": "text/plain"}, raw=b'{"defaults": {"temperature": 1}}')
+        self.assertEqual(code, 415)
+        code, _ = self.req("/settings", {"defaults": {"temperature": 1}}, {"Origin": "http://evil.example"})
+        self.assertEqual(code, 403)
+        code, b = self.req("/settings", {"defaults": {"temperature": 9}})
+        self.assertEqual(code, 400)
+        self.assertIn("temperature", b["error"]["message"])
+        self.assertEqual(self.svc.shared, {})
+        host = self.base.split("://", 1)[1]
+        code, _ = self.req("/settings", {"defaults": {"temperature": 1}}, {"Origin": "http://" + host})
+        self.assertEqual(code, 200)
+
+    def test_they_need_the_key_when_one_is_set(self):
+        self.svc.api_key = "secret"
+        try:
+            self.assertEqual(self.req("/settings", {"defaults": {"temperature": 1}})[0], 401)
+            self.assertEqual(self.req("/settings", {"defaults": {"temperature": 1}},
+                                      {"Authorization": "Bearer secret"})[0], 200)
+        finally:
+            self.svc.api_key = ""
+
+
+class WebApp(unittest.TestCase):
+    """The web app (PR #22's dashboard idea, rebuilt): its page and files, and GET /metrics."""
+
+    @classmethod
+    def setUpClass(cls):
+        tok = ByteTokenizer()
+        cls.svc = Service(RecordingEngine(tok, "</think>\n\nhello", max_context=CTX), tok,
+                          ChatTemplate(ROOT / "serve/chat_template.jinja"))
+        cls.httpd = serve(cls.svc, port=0)
+        cls.base = f"http://127.0.0.1:{cls.httpd.server_address[1]}"
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.httpd.shutdown()
+
+    def get(self, path, headers=None):
+        req = urllib.request.Request(self.base + path, headers=headers or {})
+        try:
+            with urllib.request.urlopen(req, timeout=10) as r:
+                return r.status, r.headers.get("Content-Type", ""), r.read()
+        except urllib.error.HTTPError as e:
+            return e.code, e.headers.get("Content-Type", ""), e.read()
+
+    def test_page_and_files(self):
+        code, ctype, body = self.get("/")
+        self.assertEqual(code, 200)
+        self.assertIn("text/html", ctype)
+        self.assertIn(b"/web/app.js", body)
+        for path, want in (("/web/app.js", "javascript"), ("/web/app.css", "text/css"), ("/web/tokens.css", "text/css"),
+                           ("/web/components.css", "text/css"), ("/web/sprite.svg", "image/svg+xml")):
+            with self.subTest(path=path):
+                code, ctype, _ = self.get(path)
+                self.assertEqual(code, 200)
+                self.assertIn(want, ctype)
+
+    def test_only_the_app_files_are_served(self):
+        for path in ("/web/..%2Fserver.py", "/web/index.html", "/web/test.py", "/fonts/..%2F..%2Fsetup.py",
+                     "/fonts/missing.woff2", "/fonts/x.ttf"):
+            with self.subTest(path=path):
+                self.assertEqual(self.get(path)[0], 404)
+
+    def test_metrics(self):
+        data = json.dumps({"model": "m", "messages": [{"role": "user", "content": "hi"}], "max_tokens": 5}).encode()
+        urllib.request.urlopen(urllib.request.Request(self.base + "/v1/chat/completions", data=data,
+                                                      headers={"Content-Type": "application/json"}), timeout=10).read()
+        code, ctype, body = self.get("/metrics")
+        self.assertEqual(code, 200)
+        m = json.loads(body)
+        for key in ("engine", "live", "requests", "hardware", "hardware_static", "history"):
+            self.assertIn(key, m)
+        self.assertEqual(m["engine"]["max_context"], CTX)
+        self.assertEqual(m["live"]["state"], "idle")
+        self.assertEqual(m["requests"][0]["output_tokens"], 5)
+
+    def test_metrics_need_the_key_when_one_is_set(self):
+        self.svc.api_key = "secret"
+        try:
+            self.assertEqual(self.get("/metrics")[0], 401)
+            self.assertEqual(self.get("/metrics", {"Authorization": "Bearer secret"})[0], 200)
+            self.assertEqual(self.get("/")[0], 200)                  # the page itself asks for the key
+        finally:
+            self.svc.api_key = ""
+
+
 if __name__ == "__main__":
    unittest.main()
@@ -0,0 +1,141 @@
+/* Strata web app - layout on top of tokens.css + components.css (the Strata UI kit). Colours, type, radii and
+   shadows come only from the kit's tokens. */
+[hidden] { display: none !important; }
+html, body { height: 100%; }
+body { display: flex; flex-direction: column; overflow: hidden; }
+main { flex: 1; min-height: 0; display: flex; }
+.view { flex: 1; min-height: 0; display: flex; flex-direction: column; }
+.view[hidden] { display: none; }
+.muted { color: var(--st-ink-muted); }
+.small { font-size: var(--st-fs-xs); }
+.card-title { font-weight: var(--st-fw-bold); font-size: var(--st-fs-base); color: var(--st-ink); }
+.st-header { position: relative; z-index: 2; flex: none; }
+.st-header .st-tabs { margin-left: var(--st-s-4); }
+#drawer-close .st-icon { transform: rotate(-90deg); }
+
+/* ---------------------------------------------------------------- chat */
+.chat-scroll { flex: 1; min-height: 0; overflow-y: auto; }
+.chat { max-width: 860px; margin: 0 auto; padding: var(--st-s-8) var(--st-s-6) var(--st-s-6); display: flex;
+        flex-direction: column; gap: var(--st-s-6); }
+.chat-empty { margin: 12vh auto 0; text-align: center; }
+.chat-empty__title { font-weight: var(--st-fw-black); font-size: var(--st-fs-display); line-height: var(--st-lh-tight);
+                     letter-spacing: -.02em; }
+.chat-empty__sub { margin-top: var(--st-s-3); color: var(--st-ink-muted); }
+.st-msg { width: 100%; }
+.st-msg--assistant { align-self: flex-start; }
+.st-msg--assistant .st-bubble { width: 100%; }
+.st-msg--user .st-bubble { white-space: pre-wrap; overflow-wrap: anywhere; }
+.st-bubble > :first-child { margin-top: 0; }
+.st-bubble > :last-child { margin-bottom: 0; }
+.st-bubble p, .st-bubble ul, .st-bubble ol { margin: 0 0 var(--st-s-3); }
+.st-bubble ul, .st-bubble ol { padding-left: var(--st-s-6); }
+.st-bubble li + li { margin-top: var(--st-s-1); }
+.st-bubble h3 { font-size: var(--st-fs-lg); margin: var(--st-s-4) 0 var(--st-s-2); color: var(--st-ink); }
+.st-bubble h4 { font-size: var(--st-fs-base); margin: var(--st-s-3) 0 var(--st-s-2); color: var(--st-ink); }
+.st-bubble code.inline { font: 400 .9em var(--st-font-mono); background: var(--st-surface-2); border: 1px solid var(--st-line-soft);
+                         border-radius: 6px; padding: 1px 5px; }
+.st-bubble .st-code { margin: var(--st-s-3) 0; }
+.st-bubble blockquote { margin: 0 0 var(--st-s-3); padding-left: var(--st-s-3); border-left: 3px solid var(--st-line);
+                        color: var(--st-ink-muted); }
+.st-bubble hr { border: 0; border-top: 1px solid var(--st-line); margin: var(--st-s-4) 0; }
+.st-bubble table { border-collapse: collapse; margin: 0 0 var(--st-s-3); font-size: var(--st-fs-md); }
+.st-bubble th, .st-bubble td { border: 1px solid var(--st-line); padding: 6px 10px; text-align: left; }
+.msg-images { display: flex; flex-wrap: wrap; gap: var(--st-s-2); justify-content: flex-end; }
+.msg-images img { max-width: 220px; max-height: 180px; border-radius: var(--st-r-md); border: 1px solid var(--st-line); }
+.st-msg .st-collapse { width: 100%; }
+.st-collapse__body.thinking { white-space: pre-wrap; overflow-wrap: anywhere; max-height: 420px; overflow-y: auto; }
+.st-msg__meta .st-btn--icon { width: 28px; height: 28px; }
+.st-msg__meta .st-btn--icon .st-icon { width: 16px; height: 16px; }
+.cursor::after { content: ""; display: inline-block; width: 8px; height: 1.1em; margin-left: 2px; vertical-align: -3px;
+                 border-radius: 2px; background: var(--st-accent); animation: blink 1s steps(2) infinite; }
+@keyframes blink { 50% { opacity: 0; } }
+.msg-error { color: var(--st-danger-text); background: var(--st-danger-tint); border-radius: var(--st-r-md); padding: 10px 14px;
+             font-size: var(--st-fs-md); }
+
+.composer-wrap { flex: none; padding: 0 var(--st-s-6) var(--st-s-6); }
+.st-composer { max-width: 860px; margin: 0 auto; }
+.st-composer textarea { max-height: 40vh; }
+.st-btn--send { width: 42px; padding: 0; }
+.composer-hint { font-size: var(--st-fs-xs); color: var(--st-ink-muted); margin-right: var(--st-s-2); font-variant-numeric: tabular-nums; }
+.attachments { display: flex; flex-wrap: wrap; gap: var(--st-s-2); padding: 2px 4px 0; }
+.chip { display: inline-flex; align-items: center; gap: 6px; height: 30px; padding: 0 6px 0 10px; border-radius: var(--st-r-pill);
+        background: var(--st-surface-2); border: 1px solid var(--st-line); font-size: var(--st-fs-xs); color: var(--st-ink-soft); }
+.chip .st-btn--icon { width: 22px; height: 22px; border-radius: 50%; }
+.chip .st-btn--icon .st-icon { width: 14px; height: 14px; }
+
+/* ---------------------------------------------------------------- monitor */
+.view--monitor, .view--about { overflow-y: auto; }
+.monitor, .about { max-width: 1240px; width: 100%; margin: 0 auto; padding: var(--st-s-6); display: flex; flex-direction: column;
+                   gap: var(--st-s-5); }
+.state-card { display: flex; flex-direction: column; gap: var(--st-s-3); }
+.state-card__head { display: flex; align-items: center; gap: var(--st-s-4); flex-wrap: wrap; }
+.badges { display: flex; gap: var(--st-s-2); flex-wrap: wrap; }
+.badges .st-badge { opacity: .38; transition: opacity var(--st-dur); }
+.badges .st-badge.on { opacity: 1; }
+.state-card__row { display: flex; justify-content: space-between; gap: var(--st-s-4); font-size: var(--st-fs-sm);
+                   font-weight: var(--st-fw-medium); font-variant-numeric: tabular-nums; }
+.metrics { display: grid; grid-template-columns: repeat(4, minmax(0, 1fr)); gap: var(--st-s-4); }
+.metric-card { padding: var(--st-s-4) var(--st-s-5); }
+.st-metric__value small { font-size: var(--st-fs-md); font-weight: var(--st-fw-medium); color: var(--st-ink-muted); margin-left: 4px; }
+.st-metric__sub { font-size: var(--st-fs-xs); color: var(--st-ink-muted); font-variant-numeric: tabular-nums; min-height: 1em; }
+.st-metric__spark { display: block; overflow: visible; }
+.st-metric__spark[data-tone="warn"] { color: var(--st-warn); }
+.st-metric__spark[data-tone="info"] { color: var(--st-info); }
+.monitor-row { display: grid; grid-template-columns: minmax(260px, 1fr) minmax(0, 2.4fr); gap: var(--st-s-4); }
+.ctx-card { display: flex; flex-direction: column; gap: var(--st-s-2); }
+.ctx-gauge { position: relative; width: 150px; height: 150px; margin: var(--st-s-2) auto var(--st-s-3); }
+.ctx-gauge .st-gauge { width: 150px; height: 150px; }
+.ctx-gauge .st-gauge__fill { transition: stroke-dasharray 400ms var(--st-ease); }
+.ctx-gauge__label { position: absolute; inset: 0; display: flex; flex-direction: column; align-items: center; justify-content: center;
+                    font-size: var(--st-fs-xs); }
+.ctx-gauge__pct { font-weight: var(--st-fw-black); font-size: var(--st-fs-xl); line-height: 1.1; font-variant-numeric: tabular-nums; }
+.bar-row { display: flex; justify-content: space-between; font-size: var(--st-fs-sm); font-weight: var(--st-fw-medium);
+           margin-top: var(--st-s-2); font-variant-numeric: tabular-nums; }
+.req-card { display: flex; flex-direction: column; gap: var(--st-s-3); min-width: 0; }
+.table-wrap { overflow-x: auto; }
+
+/* ---------------------------------------------------------------- about */
+.about { max-width: 860px; }
+.about .st-card { display: flex; flex-direction: column; gap: var(--st-s-3); }
+.about p { margin: 0; }
+.facts { display: grid; grid-template-columns: minmax(140px, max-content) 1fr; gap: 10px var(--st-s-6); margin: 0; font-size: var(--st-fs-md); }
+.facts dt { color: var(--st-ink-muted); }
+.facts dd { margin: 0; color: var(--st-ink-soft); font-variant-numeric: tabular-nums; display: flex; align-items: center; gap: 6px;
+            overflow-wrap: anywhere; }
+.facts dd code { font: 400 13px var(--st-font-mono); }
+.facts .st-btn--icon { width: 28px; height: 28px; }
+.facts .st-btn--icon .st-icon { width: 16px; height: 16px; }
+.toggle-row { display: flex; align-items: center; justify-content: space-between; gap: var(--st-s-4); font-size: var(--st-fs-sm);
+              font-weight: var(--st-fw-medium); color: var(--st-ink-soft); }
+.toggle-row .st-toggle { flex: none; }   /* a long label must not squeeze the switch */
+
+/* ---------------------------------------------------------------- drawer */
+.st-drawer { z-index: 40; }
+.scrim { position: fixed; inset: 0; background: rgba(15, 23, 42, .18); z-index: 39; }
+.seg { display: grid; grid-template-columns: repeat(4, 1fr); gap: 4px; padding: 4px; background: var(--st-surface-2);
+       border: 1px solid var(--st-line); border-radius: var(--st-r-md); }
+.seg button { height: 32px; border: 0; border-radius: 10px; background: transparent; color: var(--st-ink-muted);
+              font: 500 var(--st-fs-sm)/1 var(--st-font); cursor: pointer; }
+.seg button[aria-checked="true"] { background: var(--st-surface); color: var(--st-ink); font-weight: var(--st-fw-bold);
+                                   box-shadow: var(--st-sh-card); }
+.drawer-actions { display: grid; grid-template-columns: 1fr 1fr; gap: var(--st-s-3); margin-top: var(--st-s-2); }
+.st-toast { animation: toast-in 180ms var(--st-ease); }
+@keyframes toast-in { from { opacity: 0; transform: translateY(6px); } }
+
+/* ---------------------------------------------------------------- narrow screens */
+@media (max-width: 1000px) {
+  .metrics { grid-template-columns: repeat(2, minmax(0, 1fr)); }
+  .monitor-row { grid-template-columns: 1fr; }
+}
+@media (max-width: 640px) {
+  .st-header { padding: 0 var(--st-s-3); gap: var(--st-s-2); }
+  .st-header .st-tabs { margin-left: 0; }
+  .st-tab { padding: 0 10px; }
+  .st-tab .st-icon { display: none; }
+  #pill-text { display: none; }
+  .chat { padding: var(--st-s-5) var(--st-s-4); }
+  .composer-wrap { padding: 0 var(--st-s-3) var(--st-s-3); }
+  .monitor, .about { padding: var(--st-s-4); }
+  .facts { grid-template-columns: 1fr; gap: 2px; }
+  .facts dd { margin-bottom: var(--st-s-2); }
+}
@@ -0,0 +1,750 @@
+// serve/web/app.js - the Strata web app: Chat, Monitor, About. No framework, no network beyond this server.
+// The Monitor tab rebuilds PR #22's dashboard idea (code-martin) on the server's own /metrics.
+"use strict";
+
+const $ = (id) => document.getElementById(id);
+const SPRITE = "/web/sprite.svg";
+const icon = (name, cls = "st-icon") => `<svg class="${cls}" aria-hidden="true"><use href="${SPRITE}#i-${name}"/></svg>`;
+const esc = (s) => String(s).replace(/[&<>"']/g, (c) => ({"&": "&amp;", "<": "&lt;", ">": "&gt;", '"': "&quot;", "'": "&#39;"}[c]));
+const fmt = (n, d = 0) => (n == null || Number.isNaN(n) ? "–" : Number(n).toLocaleString(undefined, {maximumFractionDigits: d, minimumFractionDigits: d}));
+const kfmt = (n) => (n == null ? "–" : n >= 1000 ? `${fmt(n / 1000, n >= 10000 ? 0 : 1)}k` : fmt(n));
+// a context size: 32768 -> "32K" (powers of two), else like kfmt
+const ctxfmt = (n) => (n && n % 1024 === 0 ? `${fmt(n / 1024)}K` : kfmt(n));
+const gb = (b, d = 1) => (b == null ? "–" : fmt(b / 1073741824, d));   // memory: binary GB, as Windows shows it
+
+const store = {
+  get(k, d) { try { const v = localStorage.getItem("strata." + k); return v === null ? d : JSON.parse(v); } catch (e) { return d; } },
+  set(k, v) { try { localStorage.setItem("strata." + k, JSON.stringify(v)); } catch (e) { /* private mode: in memory only */ } },
+};
+
+// ------------------------------------------------------------------ toasts
+function toast(kind, title, text = "", ms = 3500, action = null) {
+  const names = {info: "info", success: "check", warn: "warning", error: "error"};
+  const el = document.createElement("div");
+  el.className = `st-toast st-toast--${kind}`;
+  el.innerHTML = `${icon(names[kind] || "info")}<div><div class="st-toast__title"></div><div class="t-text"></div></div>`;
+  el.querySelector(".st-toast__title").textContent = title;
+  el.querySelector(".t-text").textContent = text;
+  if (action) {
+    const b = document.createElement("button");
+    b.className = "st-btn st-btn--secondary";
+    b.style.height = "32px";
+    b.style.marginLeft = "auto";
+    b.textContent = action.label;
+    b.onclick = () => { action.run(); el.remove(); };
+    el.appendChild(b);
+  }
+  $("toasts").appendChild(el);
+  setTimeout(() => el.remove(), ms);
+}
+
+async function copyText(text, btn) {
+  try {
+    await navigator.clipboard.writeText(text);
+  } catch (e) {                                   // http on another host: no async clipboard
+    const ta = document.createElement("textarea");
+    ta.value = text; document.body.appendChild(ta); ta.select(); document.execCommand("copy"); ta.remove();
+  }
+  if (btn) {
+    const use = btn.querySelector("use");
+    use.setAttribute("href", `${SPRITE}#i-check`);
+    setTimeout(() => use.setAttribute("href", `${SPRITE}#i-copy`), 1500);
+  }
+  toast("success", "Copied to clipboard", "", 1800);
+}
+
+// ------------------------------------------------------------------ theme and tabs
+// the system's theme until the user picks one (only a click is saved)
+function setTheme(t, save) {
+  document.documentElement.dataset.theme = t;
+  if (save) try { localStorage.setItem("strata.theme", t); } catch (e) { /* ignore */ }
+  $("theme-icon").setAttribute("href", `${SPRITE}#i-${t === "dark" ? "sun" : "moon"}`);
+  $("dark-toggle").setAttribute("aria-checked", String(t === "dark"));
+}
+const flipTheme = () => setTheme(document.documentElement.dataset.theme === "dark" ? "light" : "dark", true);
+$("theme-btn").onclick = flipTheme;
+$("dark-toggle").onclick = flipTheme;
+setTheme(document.documentElement.dataset.theme || "light", false);
+matchMedia("(prefers-color-scheme: dark)").addEventListener("change", (e) => {
+  let saved = null;
+  try { saved = localStorage.getItem("strata.theme"); } catch (err) { /* ignore */ }
+  if (!saved) setTheme(e.matches ? "dark" : "light", false);
+});
+
+let tab = "chat";
+function showTab(name) {
+  tab = ["chat", "monitor", "about"].includes(name) ? name : "chat";
+  for (const b of document.querySelectorAll(".st-tab")) b.setAttribute("aria-selected", String(b.dataset.tab === tab));
+  for (const v of ["chat", "monitor", "about"]) $(`view-${v}`).hidden = v !== tab;
+  if (location.hash.slice(1) !== tab) history.replaceState(null, "", tab === "chat" ? location.pathname : `#${tab}`);
+  if (tab === "chat") $("input").focus();
+  if (lastMetrics) render(lastMetrics);
+}
+for (const b of document.querySelectorAll(".st-tab")) b.onclick = () => showTab(b.dataset.tab);
+window.addEventListener("hashchange", () => showTab(location.hash.slice(1)));
+
+// ------------------------------------------------------------------ server access
+function headers(json = false) {
+  const h = {};
+  const key = store.get("apikey", "");
+  if (key) h.Authorization = "Bearer " + key;
+  if (json) h["Content-Type"] = "application/json";
+  return h;
+}
+$("api-key").value = store.get("apikey", "");
+$("api-key").onchange = () => { store.set("apikey", $("api-key").value.trim()); toast("success", "API key saved", "Kept in this browser only."); };
+
+let health = {model: "strata", images: false, max_context: 0};
+async function loadHealth() {
+  try {
+    health = await (await fetch("/health")).json();
+    $("attach-btn").hidden = !health.images;
+    $("chat-empty-sub").textContent = `${health.model} runs on this PC. Nothing leaves it.`;
+  } catch (e) {
+    setTimeout(loadHealth, 2000);
+  }
+}
+
+// ------------------------------------------------------------------ Monitor
+const METRICS = [
+  {key: "speed", label: "Speed", icon: "gauge", unit: "tok/s", series: "tok_s"},
+  {key: "gpu", label: "GPU load", icon: "gpu", unit: "%", series: "gpu_util", max: 100},
+  {key: "vram", label: "VRAM", icon: "layers", unit: "GB", series: "gpu_mem_used"},
+  {key: "temp", label: "GPU temp", icon: "thermometer", unit: "°C", series: "gpu_temp", tone: "warn"},
+  {key: "power", label: "Power", icon: "bolt", unit: "W", series: "gpu_power"},
+  {key: "pcie", label: "PCIe", icon: "link", unit: "", series: "gpu_pcie_rx_mb", tone: "info"},
+  {key: "cpu", label: "CPU", icon: "cpu", unit: "%", series: "cpu", max: 100},
+  {key: "disk", label: "Disk read", icon: "disk", unit: "MB/s", series: "disk_read_mb", tone: "info"},
+];
+$("metrics").innerHTML = METRICS.map((m) => `
+  <div class="st-card metric-card"><div class="st-metric">
+    <span class="st-metric__label">${icon(m.icon, "st-icon st-icon--sm")}${esc(m.label)}</span>
+    <span class="st-metric__value" id="mv-${m.key}">–</span>
+    <span class="st-metric__sub" id="ms-${m.key}"></span>
+    <svg class="st-metric__spark" id="sp-${m.key}" viewBox="0 0 100 32" preserveAspectRatio="none"${m.tone ? ` data-tone="${m.tone}"` : ""}>
+      <path class="area" fill="currentColor" opacity=".12"/><path class="line" fill="none" stroke="currentColor"
+      stroke-width="1.6" stroke-linejoin="round" stroke-linecap="round" vector-effect="non-scaling-stroke"/></svg>
+  </div></div>`).join("");
+
+function spark(id, values, max) {
+  const svg = $(id);
+  const v = (values || []).map((x) => (x == null ? 0 : x));
+  if (v.length < 2) { svg.querySelector(".line").setAttribute("d", ""); svg.querySelector(".area").setAttribute("d", ""); return; }
+  const top = Math.max(max || 0, ...v, 1e-9);
+  const pts = v.map((x, i) => [(i / (v.length - 1)) * 100, 30 - (x / top) * 26]);
+  const line = pts.map((p, i) => `${i ? "L" : "M"}${p[0].toFixed(2)},${p[1].toFixed(2)}`).join("");
+  svg.querySelector(".line").setAttribute("d", line);
+  svg.querySelector(".area").setAttribute("d", `${line}L100,32L0,32Z`);
+}
+function setMetric(key, value, unit, sub) {
+  $(`mv-${key}`).innerHTML = value == null ? "–" : `${esc(value)}${unit ? `<small>${esc(unit)}</small>` : ""}`;
+  $(`ms-${key}`).textContent = sub || "";
+}
+
+let lastMetrics = null, metricsFailures = 0, keyWarned = false;
+async function poll() {
+  try {
+    const r = await fetch("/metrics", {headers: headers()});
+    if (r.status === 401) {
+      setPill("error", "API key needed");
+      if (!keyWarned) { keyWarned = true; toast("warn", "API key needed", "This server needs a key: add it under About > Settings.", 6000); }
+    } else if (r.ok) {
+      lastMetrics = await r.json();
+      metricsFailures = 0;
+      render(lastMetrics);
+    } else {
+      throw new Error(`HTTP ${r.status}`);
+    }
+  } catch (e) {
+    if (++metricsFailures === 3) setPill("error", "Server not reachable");
+  }
+  setTimeout(poll, 1000);
+}
+
+function setPill(state, text) {
+  $("pill").dataset.state = state === "error" ? "queued" : state;
+  $("pill-text").textContent = text;
+}
+
+function render(m) {
+  const live = m.live || {}, hw = m.hardware || {}, st = m.hardware_static || {}, eng = m.engine || {}, h = m.history || {};
+  const last = (m.requests || [])[0];
+  // the header pill
+  if (live.state === "reading") {
+    const pct = live.prompt_total ? Math.round((100 * live.prompt_read) / live.prompt_total) : null;
+    setPill("reading", pct != null ? `Reading prompt · ${pct}%` : "Reading prompt");
+  } else if (live.state === "generating") {
+    setPill("generating", `Generating · ${fmt(live.tok_s, 1)} tok/s`);
+  } else {
+    setPill("idle", "Idle");
+  }
+  if (live.queued > 0) setPill("queued", `${live.queued} queued`);
+  if (tab === "monitor") renderMonitor(live, hw, st, eng, h, last, m.requests || []);
+  if (tab === "about") renderAbout(eng, hw, st);
+}
+
+function renderMonitor(live, hw, st, eng, h, last, requests) {
+  // model state
+  const on = live.queued > 0 ? "queued" : live.state;
+  for (const b of document.querySelectorAll("#state-badges .st-badge")) b.classList.toggle("on", b.dataset.s === on || b.dataset.s === live.state);
+  const prog = $("state-progress");
+  let label = "Waiting for a request", detail = "", pct = 0;
+  if (live.state === "reading") {
+    label = "Reading prompt";
+    prog.dataset.tone = "info";
+    if (live.prompt_total) {
+      pct = (100 * live.prompt_read) / live.prompt_total;
+      detail = `${fmt(live.prompt_read)} / ${fmt(live.prompt_total)} tokens · ${fmt(pct)}%`;
+    } else {
+      detail = `${fmt(live.prompt_tokens)} tokens`;
+    }
+  } else if (live.state === "generating") {
+    label = live.phase ? live.phase[0].toUpperCase() + live.phase.slice(1) : "Generating";
+    delete prog.dataset.tone;
+    pct = live.max_tokens ? Math.min(100, (100 * live.generated) / live.max_tokens) : 0;
+    detail = `${fmt(live.generated)} tokens · ${fmt(live.tok_s, 1)} tok/s`;
+  } else if (last) {
+    delete prog.dataset.tone;
+    detail = `last: ${fmt(last.output_tokens)} tokens${last.decode_tok_s ? ` at ${fmt(last.decode_tok_s, 1)} tok/s` : ""}`;
+  }
+  $("state-label").textContent = label;
+  $("state-detail").textContent = detail;
+  $("state-bar").style.width = `${pct}%`;
+
+  // the eight cards
+  const speed = live.state === "generating" ? live.tok_s : last ? last.decode_tok_s : null;
+  setMetric("speed", speed == null ? null : fmt(speed, 1), "tok/s", live.state === "generating" ? "now" : last ? "last request" : "");
+  spark("sp-speed", h.tok_s);
+  setMetric("gpu", hw.gpu_util == null ? null : fmt(hw.gpu_util), "%", st.gpu_name || "");
+  spark("sp-gpu", h.gpu_util, 100);
+  setMetric("vram", hw.gpu_mem_used == null ? null : gb(hw.gpu_mem_used), hw.gpu_mem_total ? `/ ${gb(hw.gpu_mem_total, 0)} GB` : "GB",
+            eng.expert_slots ? `${fmt(eng.expert_slots)} experts cached` : "");
+  spark("sp-vram", h.gpu_mem_used, hw.gpu_mem_total);
+  setMetric("temp", hw.gpu_temp == null ? null : fmt(hw.gpu_temp), "°C", "");
+  spark("sp-temp", h.gpu_temp, 90);
+  setMetric("power", hw.gpu_power == null ? null : fmt(hw.gpu_power), "W", hw.gpu_power_limit ? `of ${fmt(hw.gpu_power_limit)} W limit` : "");
+  spark("sp-power", h.gpu_power, hw.gpu_power_limit);
+  const gen = hw.gpu_pcie_gen_max || hw.gpu_pcie_gen;
+  setMetric("pcie", gen ? `Gen${gen}` : null, hw.gpu_pcie_width ? `x${hw.gpu_pcie_width}` : "",
+            hw.gpu_pcie_rx_mb == null ? "" : `to GPU ${fmt(hw.gpu_pcie_rx_mb, hw.gpu_pcie_rx_mb < 10 ? 1 : 0)} MB/s` +
+            (hw.gpu_pcie_gen && gen && hw.gpu_pcie_gen < gen ? ` · idle Gen${hw.gpu_pcie_gen}` : ""));
+  spark("sp-pcie", h.gpu_pcie_rx_mb);
+  setMetric("cpu", hw.cpu == null ? null : fmt(hw.cpu), "%", st.threads ? `${st.cores ? `${st.cores} cores · ` : ""}${st.threads} threads` : "");
+  spark("sp-cpu", h.cpu, 100);
+  if (hw.disk_read_mb == null) {
+    setMetric("disk", null, "", st.psutil ? "" : "needs psutil (setup installs it)");
+  } else {
+    const big = hw.disk_read_mb >= 1000;
+    setMetric("disk", big ? fmt(hw.disk_read_mb / 1024, 2) : fmt(hw.disk_read_mb, hw.disk_read_mb < 10 ? 1 : 0), big ? "GB/s" : "MB/s",
+              hw.disk_write_mb == null ? "" : `write ${fmt(hw.disk_write_mb, 1)} MB/s`);
+  }
+  spark("sp-disk", h.disk_read_mb);
+
+  // context fill: the running request, else the last one
+  const ctx = eng.max_context || 0;
+  let used = 0;
+  if (live.state !== "idle") used = (live.prompt_tokens || 0) + (live.generated || 0);
+  else if (last) used = (last.prompt_tokens || 0) + (last.output_tokens || 0);
+  const frac = ctx ? Math.min(1, used / ctx) : 0;
+  $("ctx-fill").setAttribute("stroke-dasharray", `${(235.6 * frac).toFixed(1)} 314.2`);
+  $("ctx-fill").style.opacity = 235.6 * frac >= 3 ? "1" : "0";         // a near-zero arc would draw just its round cap
+  $("ctx-pct").textContent = `${Math.round(frac * 100)}%`;
+  $("ctx-sub").textContent = ctx ? `${kfmt(used)} / ${ctxfmt(ctx)}` : "–";
+  const cacheBytes = (eng.expert_cache_mib || 0) * 1048576;
+  $("slots-text").textContent = eng.expert_slots ? `${fmt(eng.expert_slots)} · ${gb(cacheBytes)} GB` : "–";
+  $("slots-bar").style.width = hw.gpu_mem_total ? `${Math.min(100, (100 * cacheBytes) / hw.gpu_mem_total)}%` : "0%";
+  $("ram-text").textContent = hw.ram_total ? `${gb(hw.ram_used)} / ${gb(hw.ram_total, 0)} GB` : "–";
+  const ramPct = hw.ram_total ? (100 * hw.ram_used) / hw.ram_total : 0;
+  $("ram-bar").style.width = `${ramPct}%`;
+  if (ramPct > 92) $("ram-progress").dataset.tone = "danger"; else delete $("ram-progress").dataset.tone;
+  $("temp-text").textContent = hw.gpu_temp == null ? "–" : `${fmt(hw.gpu_temp)} °C`;
+  $("temp-bar").style.width = hw.gpu_temp == null ? "0%" : `${Math.min(100, hw.gpu_temp)}%`;
+
+  // recent requests
+  const body = $("req-body");
+  if (!requests.length) {
+    body.innerHTML = `<tr><td colspan="7" class="muted">No requests yet</td></tr>`;
+  } else {
+    const badge = {stop: ["", "Done"], length: ["", "Max tokens"], cancel: ["st-badge--queued", "Stopped"],
+                   disconnect: ["st-badge--queued", "Closed"], error: ["st-badge--error", "Error"]};
+    body.innerHTML = requests.slice(0, 12).map((r) => {
+      const [cls, text] = badge[r.finish] || ["", r.finish || "–"];
+      const t = new Date(r.time * 1000).toLocaleTimeString([], {hour: "2-digit", minute: "2-digit", second: "2-digit"});
+      const proj = r.projection == null ? "" : ` <span class="st-badge${r.projection ? " st-badge--reading" : ""}" title="experimental speed projection ${r.projection ? "on" : "off"}">${r.projection ? "ESP" : "stock"}</span>`;
+      return `<tr><td>${esc(t)}</td><td><span class="st-badge ${cls}">${esc(text)}</span>${proj}</td><td class="num">${fmt(r.prompt_tokens)}</td>
+        <td class="num">${fmt(r.reused)}</td><td class="num">${fmt(r.output_tokens)}</td><td class="num">${fmt(r.decode_tok_s, 1)}</td>
+        <td class="num">${fmt(r.duration_s, 1)} s</td></tr>`;
+    }).join("");
+  }
+}
+
+function facts(el, rows) {
+  el.innerHTML = rows.filter((r) => r[1] != null && r[1] !== "").map(([k, v, copy]) =>
+    `<dt>${esc(k)}</dt><dd>${copy ? `<code>${esc(v)}</code><button class="st-btn st-btn--icon" data-copy="${esc(v)}" aria-label="Copy">${icon("copy")}</button>` : esc(v)}</dd>`).join("");
+}
+// INFO cvec=project:4-44[:singleL] | add:A-B | 0
+function projectionText(c) {
+  if (!c || c === "0" || c === 0) return null;
+  const [mode, range, single] = String(c).split(":");
+  const [a, b] = (range || "").split("-");
+  return `${mode === "project" ? "Projection" : "Additive"} control vector on layers ${a}–${b}` +
+         `${single ? ` (layer ${single.replace("single", "")}'s direction)` : ""}. Per chat in Sampling. Its package ` +
+         "describes the vector as a refusal-direction projection; measure the speed yourself";
+}
+function renderAbout(eng, hw, st) {
+  const kv = {int8: "8-bit", q4_0: "4-bit (Hadamard-rotated)", fp16: "16-bit"}[eng.kv] || eng.kv;
+  facts($("facts-engine"), [
+    ["Model", eng.model],
+    ["Engine", eng.version ? `v${eng.version}` : "built from source"],
+    ["Context", eng.max_context ? `${fmt(eng.max_context)} tokens` : null],
+    ["KV cache", kv ? `${kv}${eng.kv_resident ? `, streamed: ${fmt(eng.kv_resident)} positions per layer in VRAM, the rest in RAM` : ", all in VRAM"}` : null],
+    ["Experts in VRAM", eng.expert_slots ? `${fmt(eng.expert_slots)} (${gb((eng.expert_cache_mib || 0) * 1048576)} GB)` : null],
+    ["Speculation", eng.spec ? `MTP drafts up to ${Math.max(0, (eng.mtp_max || eng.spec) - 1)} tokens${eng.lookup ? ", prompt lookup on" : ""}` : null],
+    ["Images", eng.images ? "on" : "off"],
+    ["Experimental speed projection", projectionText(eng.cvec)],
+  ]);
+  facts($("facts-hw"), [
+    ["GPU", st.gpu_name ? `${st.gpu_name}${hw.gpu_mem_total ? `, ${gb(hw.gpu_mem_total, 0)} GB` : ""}` : "not readable (NVML)"],
+    ["CPU", st.cpu_name ? `${st.cpu_name}${st.threads ? `, ${st.threads} threads` : ""}` : null],
+    ["RAM", hw.ram_total ? `${gb(hw.ram_total, 0)} GB` : null],
+  ]);
+  const base = location.origin;
+  facts($("facts-api"), [
+    ["OpenAI base URL", `${base}/v1`, true],
+    ["Anthropic base URL", base, true],
+    ["Model name", eng.model, true],
+  ]);
+}
+document.addEventListener("click", (e) => {
+  const b = e.target.closest("[data-copy]");
+  if (b) copyText(b.dataset.copy, b);
+});
+
+// ------------------------------------------------------------------ Markdown (escaped first, then formatted)
+function inline(s) {
+  const codes = [];
+  s = s.replace(/`([^`\n]+)`/g, (_, c) => { codes.push(c); return `\u0000${codes.length - 1}\u0000`; });
+  s = esc(s)
+    .replace(/\*\*([^*\n]+)\*\*/g, "<strong>$1</strong>")
+    .replace(/(^|[^*\w])\*([^*\n]+)\*(?![*\w])/g, "$1<em>$2</em>")
+    .replace(/\[([^\]\n]+)\]\((https?:\/\/[^)\s]+)\)/g, '<a href="$2" target="_blank" rel="noopener noreferrer">$1</a>');
+  return s.replace(/\u0000(\d+)\u0000/g, (_, i) => `<code class="inline">${esc(codes[+i])}</code>`);
+}
+function codeBlock(lang, code) {
+  return `<div class="st-code"><div class="st-code__head"><span>${esc(lang || "code")}</span>` +
+    `<button class="st-btn st-btn--icon" data-code-copy aria-label="Copy code">${icon("copy")}</button></div>` +
+    `<pre><code>${esc(code)}</code></pre></div>`;
+}
+function blocks(text) {
+  const out = [], lines = text.split("\n");
+  let para = [], list = null;
+  const flushPara = () => { if (para.length) out.push(`<p>${para.map(inline).join("<br>")}</p>`); para = []; };
+  const flushList = () => { if (list) out.push(`<${list.tag}>${list.items.map((i) => `<li>${inline(i)}</li>`).join("")}</${list.tag}>`); list = null; };
+  for (let i = 0; i < lines.length; i++) {
+    const l = lines[i];
+    let m;
+    if (!l.trim()) { flushPara(); flushList(); continue; }
+    if ((m = l.match(/^(#{1,6})\s+(.*)$/))) { flushPara(); flushList(); out.push(`<${m[1].length <= 2 ? "h3" : "h4"}>${inline(m[2])}</${m[1].length <= 2 ? "h3" : "h4"}>`); continue; }
+    if (/^\s*([-*_])\s*\1\s*\1[\s\1]*$/.test(l)) { flushPara(); flushList(); out.push("<hr>"); continue; }
+    if ((m = l.match(/^>\s?(.*)$/))) { flushPara(); flushList(); out.push(`<blockquote>${inline(m[1])}</blockquote>`); continue; }
+    if (/^\s*\|.*\|\s*$/.test(l) && i + 1 < lines.length && /^\s*\|?[\s:-]+\|[\s|:-]*$/.test(lines[i + 1])) {
+      flushPara(); flushList();
+      const cells = (row) => row.trim().replace(/^\||\|$/g, "").split("|").map((c) => inline(c.trim()));
+      let html = `<table><thead><tr>${cells(l).map((c) => `<th>${c}</th>`).join("")}</tr></thead><tbody>`;
+      i += 2;
+      while (i < lines.length && /^\s*\|.*\|\s*$/.test(lines[i])) html += `<tr>${cells(lines[i++]).map((c) => `<td>${c}</td>`).join("")}</tr>`;
+      i--;
+      out.push(html + "</tbody></table>");
+      continue;
+    }
+    if ((m = l.match(/^\s*(?:[-*+]|(\d+)[.)])\s+(.*)$/))) {
+      flushPara();
+      const tag = m[1] ? "ol" : "ul";
+      if (!list || list.tag !== tag) { flushList(); list = {tag, items: []}; }
+      list.items.push(m[2]);
+      continue;
+    }
+    if (list && /^\s{2,}\S/.test(l)) { list.items[list.items.length - 1] += " " + l.trim(); continue; }
+    flushList();
+    para.push(l);
+  }
+  flushPara(); flushList();
+  return out.join("");
+}
+function markdown(text) {
+  let html = "", rest = text;
+  for (;;) {
+    const m = rest.match(/(^|\n)```([^\n`]*)\n/);
+    if (!m) { html += blocks(rest); break; }
+    html += blocks(rest.slice(0, m.index));
+    rest = rest.slice(m.index + m[0].length);
+    const end = rest.match(/(^|\n)```[ \t]*(\n|$)/);
+    if (!end) { html += codeBlock(m[2].trim(), rest); break; }         // still streaming
+    html += codeBlock(m[2].trim(), rest.slice(0, end.index));
+    rest = rest.slice(end.index + end[0].length);
+  }
+  return html;
+}
+
+// ------------------------------------------------------------------ Chat
+const DEFAULTS = {thinking: "high", temperature: 0.6, top_p: 0.95, top_k: 20, max: "", seed: "", show: true, esp: true};
+let settings = {...DEFAULTS, ...store.get("sampling", {})};
+let messages = store.get("chat", []);
+let attachments = [];                 // {name, url}
+let busy = null;                      // {controller, msg}
+
+function saveChat() {
+  store.set("chat", messages.map((m) => ({...m, images: (m.images || []).map((i) => ({name: i.name}))})));
+}
+function timeStr(t) { return new Date(t).toLocaleTimeString([], {hour: "2-digit", minute: "2-digit"}); }
+
+function msgEl(m, i) {
+  const el = document.createElement("div");
+  el.className = `st-msg st-msg--${m.role}`;
+  el.dataset.i = i;
+  if (m.role === "user") {
+    if (m.images && m.images.length) {
+      const wrap = document.createElement("div");
+      wrap.className = "msg-images";
+      for (const im of m.images) {
+        if (im.url) { const img = document.createElement("img"); img.src = im.url; img.alt = im.name || "image"; wrap.appendChild(img); }
+        else { const c = document.createElement("span"); c.className = "chip"; c.innerHTML = icon("image", "st-icon st-icon--sm"); c.append(im.name || "image"); wrap.appendChild(c); }
+      }
+      el.appendChild(wrap);
+    }
+    const b = document.createElement("div");
+    b.className = "st-bubble";
+    b.textContent = m.text;
+    el.appendChild(b);
+    const meta = document.createElement("div");
+    meta.className = "st-msg__meta";
+    meta.textContent = `You · ${timeStr(m.time)}`;
+    el.appendChild(meta);
+  } else {
+    el.innerHTML = `<details class="st-collapse" hidden><summary>${icon("thinking", "st-icon st-icon--sm")}<span class="think-title"></span>` +
+      `${icon("chevron", "st-icon st-icon--sm st-chev")}</summary><div class="st-collapse__body thinking"></div></details>` +
+      `<div class="st-bubble"></div><div class="st-msg__meta"><span class="meta-text"></span>` +
+      `<button class="st-btn st-btn--icon" data-msg-copy aria-label="Copy the answer" title="Copy">${icon("copy")}</button></div>`;
+    updateAssistant(el, m, false);
+  }
+  return el;
+}
+function updateAssistant(el, m, streaming) {
+  const det = el.querySelector("details");
+  if (m.reasoning) {
+    det.hidden = false;
+    const thinkingNow = streaming && !m.text;
+    el.querySelector(".think-title").textContent = thinkingNow ? "Thinking…" :
+      m.thinkSecs != null ? `Thought for ${fmt(m.thinkSecs, 1)} s` : "Thoughts";
+    const body = el.querySelector(".thinking");
+    if (det.open || thinkingNow) body.textContent = m.reasoning;
+    else body.dataset.pending = "1";
+    // open while it streams (if wanted), closed once the answer starts - unless the user toggled it themselves
+    if (thinkingNow && settings.show && !det.dataset.touched && !det.open) { det._auto = true; det.open = true; }
+    if (!thinkingNow && det.open && !det.dataset.touched) { det._auto = true; det.open = false; }
+  }
+  const bubble = el.querySelector(".st-bubble");
+  if (m.error) {
+    bubble.innerHTML = `<div class="msg-error"></div>`;
+    bubble.firstChild.textContent = m.error;
+  } else if (!m.text && streaming) {
+    bubble.innerHTML = m.reasoning ? `<span class="muted cursor">Writing</span>` : `<span class="cursor"></span>`;
+  } else {
+    bubble.innerHTML = markdown(m.text || "");
+    if (streaming) bubble.classList.add("cursor"); else bubble.classList.remove("cursor");
+  }
+  el.querySelector(".meta-text").textContent = m.meta || (streaming ? "" : m.stopped ? "Stopped" : "");
+  el.querySelector("[data-msg-copy]").hidden = streaming || !m.text;
+}
+function renderChat() {
+  const chat = $("chat");
+  chat.querySelectorAll(".st-msg").forEach((e) => e.remove());
+  $("chat-empty").hidden = messages.length > 0;
+  messages.forEach((m, i) => chat.appendChild(msgEl(m, i)));
+  scrollDown(true);
+}
+function nearBottom() { const s = $("chat-scroll"); return s.scrollHeight - s.scrollTop - s.clientHeight < 120; }
+function scrollDown(force) { const s = $("chat-scroll"); if (force || nearBottom()) s.scrollTop = s.scrollHeight; }
+
+$("chat").addEventListener("click", (e) => {
+  const cc = e.target.closest("[data-code-copy]");
+  if (cc) { copyText(cc.closest(".st-code").querySelector("pre").textContent, cc); return; }
+  const mc = e.target.closest("[data-msg-copy]");
+  if (mc) { const i = +mc.closest(".st-msg").dataset.i; copyText(messages[i].text, mc); }
+});
+$("chat").addEventListener("toggle", (e) => {
+  const d = e.target;
+  if (d.tagName !== "DETAILS") return;
+  if (d._auto) { d._auto = false; return; }          // our own open/close, not the user's
+  d.dataset.touched = "1";
+  const body = d.querySelector(".thinking");
+  if (d.open && body.dataset.pending) { body.textContent = messages[+d.closest(".st-msg").dataset.i].reasoning; delete body.dataset.pending; }
+}, true);
+
+function apiMessages() {
+  const out = [];
+  for (const m of messages) {
+    if (m.role === "user") {
+      const imgs = (m.images || []).filter((i) => i.url);
+      out.push({role: "user", content: imgs.length ? [{type: "text", text: m.text},
+        ...imgs.map((i) => ({type: "image_url", image_url: {url: i.url}}))] : m.text});
+    } else if (m.text && !m.error) {
+      out.push({role: "assistant", content: m.text});
+    }
+  }
+  return out;
+}
+
+function setBusy(on) {
+  $("stop-btn").hidden = !on;
+  $("send-btn").disabled = on;
+  $("composer-hint").textContent = on ? "" : "Shift+Enter: new line";
+}
+
+async function send() {
+  const text = $("input").value.trim();
+  if ((!text && !attachments.length) || busy) return;
+  messages.push({role: "user", text, images: attachments, time: Date.now()});
+  attachments = [];
+  renderAttachments();
+  $("input").value = "";
+  autosize();
+  const m = {role: "assistant", text: "", reasoning: "", time: Date.now()};
+  messages.push(m);
+  renderChat();
+  const el = $("chat").lastElementChild;
+  const controller = new AbortController();
+  busy = {controller, msg: m};
+  setBusy(true);
+
+  const body = {model: health.model, messages: apiMessages(), stream: true,
+                reasoning_effort: settings.thinking};
+  if (settings.temperature > 0) {
+    Object.assign(body, {temperature: +settings.temperature, top_p: +settings.top_p, top_k: +settings.top_k});
+  } else {
+    body.temperature = 0;
+  }
+  if (settings.seed) body.seed = +settings.seed;
+  if (settings.max) body.max_tokens = +settings.max;
+  if (projectionLoaded()) body.experimental_speed_projection = !!settings.esp;
+
+  let firstAt = null, thinkStart = null, usage = null, frame = 0;
+  const paint = () => { frame = 0; updateAssistant(el, m, true); scrollDown(); };
+  try {
+    const r = await fetch("/v1/chat/completions", {method: "POST", headers: headers(true), body: JSON.stringify(body),
+                                                   signal: controller.signal});
+    if (!r.ok) {
+      let msg = `HTTP ${r.status}`;
+      try { msg = (await r.json()).error.message || msg; } catch (e) { /* not json */ }
+      if (r.status === 401) msg = "This server needs an API key: add it under About > Settings.";
+      throw new Error(msg);
+    }
+    const reader = r.body.getReader(), dec = new TextDecoder();
+    let buf = "";
+    for (;;) {
+      const {value, done} = await reader.read();
+      if (done) break;
+      buf += dec.decode(value, {stream: true});
+      let nl;
+      while ((nl = buf.indexOf("\n")) >= 0) {
+        const line = buf.slice(0, nl).trim();
+        buf = buf.slice(nl + 1);
+        if (!line.startsWith("data:")) continue;              // ": keep-alive" comments while a long prompt is read
+        const data = line.slice(5).trim();
+        if (data === "[DONE]") continue;
+        let j;
+        try { j = JSON.parse(data); } catch (e) { continue; }
+        if (j.error) throw new Error(j.error.message || "the engine reported an error");
+        if (j.usage) usage = j.usage;
+        const d = (j.choices && j.choices[0] && j.choices[0].delta) || {};
+        if (d.reasoning_content) { if (!firstAt) firstAt = performance.now(); if (!thinkStart) thinkStart = performance.now(); m.reasoning += d.reasoning_content; }
+        if (d.content) {
+          if (!firstAt) firstAt = performance.now();
+          if (thinkStart && m.thinkSecs == null) m.thinkSecs = (performance.now() - thinkStart) / 1000;
+          m.text += d.content;
+        }
+        if (!frame) frame = requestAnimationFrame(paint);
+      }
+    }
+  } catch (e) {
+    if (e.name === "AbortError") m.stopped = true;
+    else { m.error = e.message || String(e); toast("error", "The request failed", m.error, 6000); }
+  }
+  if (thinkStart && m.thinkSecs == null) m.thinkSecs = (performance.now() - thinkStart) / 1000;
+  const n = usage ? usage.completion_tokens : null;
+  if (n && firstAt) {
+    const secs = (performance.now() - firstAt) / 1000;
+    m.meta = `${fmt(n)} tokens${secs > 0.25 ? ` · ${fmt(n / secs, 1)} tok/s` : ""}${m.stopped ? " · stopped" : ""}` +
+             (projectionLoaded() ? (settings.esp ? " · projection on" : " · projection off") : "");
+  } else if (m.stopped) {
+    m.meta = "Stopped";
+  }
+  busy = null;
+  setBusy(false);
+  if (frame) cancelAnimationFrame(frame);
+  updateAssistant(el, m, false);
+  saveChat();
+  scrollDown();
+}
+
+$("composer").onsubmit = (e) => { e.preventDefault(); send(); };
+$("stop-btn").onclick = () => { if (busy) busy.controller.abort(); };
+$("input").addEventListener("keydown", (e) => {
+  if (e.key === "Enter" && !e.shiftKey && !e.isComposing) { e.preventDefault(); send(); }
+});
+function autosize() { const t = $("input"); t.style.height = "auto"; t.style.height = `${Math.min(t.scrollHeight, innerHeight * 0.4)}px`; }
+$("input").addEventListener("input", autosize);
+
+$("new-btn").onclick = () => {
+  if (busy) { toast("warn", "Still writing", "Stop the answer first."); return; }
+  if (!messages.length) return;
+  const backup = messages;
+  messages = [];
+  saveChat();
+  renderChat();
+  toast("info", "New chat", "The last one was cleared.", 6000, {label: "Undo", run: () => { messages = backup; saveChat(); renderChat(); }});
+};
+$("export-btn").onclick = () => {
+  if (!messages.length) { toast("info", "Nothing to save yet"); return; }
+  const md = messages.map((m) => m.role === "user" ? `## You\n\n${m.text}\n` :
+    `## ${health.model}\n\n${m.reasoning ? `<details><summary>Thinking</summary>\n\n${m.reasoning}\n\n</details>\n\n` : ""}${m.text || m.error || ""}\n`).join("\n");
+  const a = document.createElement("a");
+  a.href = URL.createObjectURL(new Blob([md], {type: "text/markdown"}));
+  a.download = `strata-chat-${new Date().toISOString().slice(0, 16).replace(/[:T]/g, "-")}.md`;
+  a.click();
+  setTimeout(() => URL.revokeObjectURL(a.href), 5000);
+};
+
+// pictures: the attach button, or paste into the message box
+function addFiles(files) {
+  for (const f of files) {
+    if (!f.type.startsWith("image/")) continue;
+    if (f.size > 20e6) { toast("warn", "Picture too large", `${f.name} is over 20 MB.`); continue; }
+    const r = new FileReader();
+    r.onload = () => { attachments.push({name: f.name || "pasted image", url: r.result}); renderAttachments(); };
+    r.readAsDataURL(f);
+  }
+}
+function renderAttachments() {
+  const box = $("attachments");
+  box.hidden = !attachments.length;
+  box.innerHTML = "";
+  attachments.forEach((a, i) => {
+    const c = document.createElement("span");
+    c.className = "chip";
+    c.innerHTML = icon("image", "st-icon st-icon--sm");
+    c.append(a.name);
+    const x = document.createElement("button");
+    x.type = "button"; x.className = "st-btn st-btn--icon"; x.setAttribute("aria-label", "Remove");
+    x.innerHTML = icon("trash");
+    x.onclick = () => { attachments.splice(i, 1); renderAttachments(); };
+    c.appendChild(x);
+    box.appendChild(c);
+  });
+}
+$("attach-btn").onclick = () => $("file").click();
+$("file").onchange = () => { addFiles($("file").files); $("file").value = ""; };
+$("input").addEventListener("paste", (e) => {
+  if (!health.images) return;
+  const files = [...(e.clipboardData || {}).files || []].filter((f) => f.type.startsWith("image/"));
+  if (files.length) { e.preventDefault(); addFiles(files); }
+});
+
+// ------------------------------------------------------------------ the sampling drawer
+function openDrawer(open) {
+  $("drawer").dataset.open = String(open);
+  $("drawer").setAttribute("aria-hidden", String(!open));
+  $("scrim").hidden = !open;
+  if (open) { loadDrawer(); loadShared(); }
+}
+function loadDrawer(s = settings) {
+  for (const b of $("s-thinking").children) b.setAttribute("aria-checked", String(b.dataset.v === s.thinking));
+  $("s-temp").value = s.temperature; $("s-topp").value = s.top_p; $("s-topk").value = s.top_k;
+  $("s-max").value = s.max; $("s-seed").value = s.seed;
+  $("s-show").setAttribute("aria-checked", String(!!s.show));
+  $("s-esp").setAttribute("aria-checked", String(s.esp !== false));
+  $("esp-row").hidden = !projectionLoaded();
+  $("s-share").setAttribute("aria-checked", String(sharedOn));
+  outputs();
+}
+// "Use for other apps too": the server keeps these settings as every client's defaults (GET/POST /settings)
+let sharedOn = false;
+async function loadShared() {
+  try {
+    const r = await fetch("/settings", {headers: headers()});
+    if (r.ok) sharedOn = !!(await r.json()).shared;
+  } catch (e) { /* an older server: the switch just stays off */ }
+  $("s-share").setAttribute("aria-checked", String(sharedOn));
+}
+function sharedDefaults(s) {
+  const d = {reasoning_effort: s.thinking, temperature: +s.temperature};
+  if (+s.temperature > 0) Object.assign(d, {top_p: +s.top_p, top_k: +s.top_k});
+  if (s.seed) d.seed = +s.seed;
+  if (s.max) d.max_tokens = +s.max;
+  if (projectionLoaded()) d.experimental_speed_projection = s.esp !== false;
+  return d;
+}
+async function saveShared(on, s) {
+  const r = await fetch("/settings", {method: "POST", headers: headers(true),
+                                      body: JSON.stringify({defaults: on ? sharedDefaults(s) : null})});
+  if (!r.ok) {
+    let msg = `HTTP ${r.status}`;
+    try { msg = (await r.json()).error.message || msg; } catch (e) { /* not json */ }
+    throw new Error(msg);
+  }
+  sharedOn = !!(await r.json()).shared;
+}
+// the engine was started with the experimental-speed-projection control vector (INFO cvec=...)
+function projectionLoaded() {
+  const c = lastMetrics && lastMetrics.engine ? lastMetrics.engine.cvec : 0;
+  return !!c && c !== "0";
+}
+function outputs() {
+  const t = +$("s-temp").value;
+  $("o-temp").textContent = t === 0 ? "0 · greedy" : t.toFixed(2);
+  $("o-topp").textContent = (+$("s-topp").value).toFixed(2);
+  $("o-topk").textContent = $("s-topk").value;
+  const sel = [...$("s-thinking").children].find((b) => b.getAttribute("aria-checked") === "true");
+  $("o-thinking").textContent = sel ? {none: "answers right away", low: "short", medium: "medium", high: "thorough (default)"}[sel.dataset.v] : "";
+  for (const id of ["s-topp", "s-topk"]) $(id).disabled = t === 0;
+}
+for (const b of $("s-thinking").children) b.onclick = () => { for (const x of $("s-thinking").children) x.setAttribute("aria-checked", String(x === b)); outputs(); };
+for (const id of ["s-temp", "s-topp", "s-topk"]) $(id).oninput = outputs;
+$("s-show").onclick = () => $("s-show").setAttribute("aria-checked", String($("s-show").getAttribute("aria-checked") !== "true"));
+$("s-esp").onclick = () => $("s-esp").setAttribute("aria-checked", String($("s-esp").getAttribute("aria-checked") !== "true"));
+$("s-share").onclick = () => $("s-share").setAttribute("aria-checked", String($("s-share").getAttribute("aria-checked") !== "true"));
+$("s-reset").onclick = () => loadDrawer(DEFAULTS);
+$("s-apply").onclick = async () => {
+  const sel = [...$("s-thinking").children].find((b) => b.getAttribute("aria-checked") === "true");
+  settings = {thinking: sel ? sel.dataset.v : "high", temperature: +$("s-temp").value, top_p: +$("s-topp").value,
+              top_k: +$("s-topk").value, max: $("s-max").value.trim(), seed: $("s-seed").value.trim(),
+              show: $("s-show").getAttribute("aria-checked") === "true",
+              esp: $("s-esp").getAttribute("aria-checked") === "true"};
+  store.set("sampling", settings);
+  const share = $("s-share").getAttribute("aria-checked") === "true";
+  openDrawer(false);
+  if (share || sharedOn) {
+    try {
+      await saveShared(share, settings);
+      toast("success", "Sampling saved", share ? "Other apps (omp, API clients) use these settings from their next request."
+                                               : "Other apps use their own settings again.");
+    } catch (e) {
+      toast("error", "Saved here, but not for other apps", e.message, 6000);
+    }
+    return;
+  }
+  toast("success", "Sampling saved", settings.temperature === 0 ? "Greedy: the same question gives the same answer." : "");
+};
+$("sampling-btn").onclick = () => openDrawer(true);
+$("drawer-close").onclick = () => openDrawer(false);
+$("scrim").onclick = () => openDrawer(false);
+document.addEventListener("keydown", (e) => { if (e.key === "Escape" && $("drawer").dataset.open === "true") openDrawer(false); });
+
+// ------------------------------------------------------------------ start
+setBusy(false);
+renderChat();
+const startQuestion = new URLSearchParams(location.search).get("q");   // /?q=... starts a chat (a shortcut)
+if (startQuestion) history.replaceState(null, "", location.pathname + location.hash);
+loadHealth().then(() => { if (startQuestion) { $("input").value = startQuestion; send(); } });
+showTab(location.hash.slice(1) || "chat");
+poll();
@@ -0,0 +1,128 @@
+/* Strata UI — components. Requires tokens.css. */
+*,*::before,*::after{box-sizing:border-box}
+html,body{margin:0;background:var(--st-bg);color:var(--st-ink);font-family:var(--st-font);font-size:var(--st-fs-base);line-height:var(--st-lh-body);-webkit-font-smoothing:antialiased}
+a{color:var(--st-accent-text);text-decoration:none} a:hover{text-decoration:underline}
+:focus-visible{outline:2px solid var(--st-focus);outline-offset:2px}
+
+/* icon — <svg class="st-icon"><use href="icons/sprite.svg#i-chat"/></svg> */
+.st-icon{width:20px;height:20px;flex:none;color:inherit}
+.st-icon--sm{width:16px;height:16px} .st-icon--lg{width:24px;height:24px}
+
+/* header */
+.st-header{display:flex;align-items:center;gap:var(--st-s-4);height:60px;padding:0 var(--st-s-6);background:var(--st-surface);border-bottom:1px solid var(--st-line)}
+.st-header__brand{font-weight:900;font-size:var(--st-fs-lg);letter-spacing:-.01em}
+.st-header__spacer{flex:1}
+
+/* status pill */
+.st-pill{display:inline-flex;align-items:center;gap:8px;height:30px;padding:0 12px;border-radius:var(--st-r-pill);border:1px solid var(--st-line);background:var(--st-surface-2);font-size:var(--st-fs-sm);font-weight:500;color:var(--st-ink-soft);font-variant-numeric:tabular-nums}
+.st-pill__dot{width:8px;height:8px;border-radius:50%;background:var(--st-ink-muted)}
+.st-pill[data-state="generating"] .st-pill__dot{background:var(--st-accent);box-shadow:0 0 0 4px var(--st-accent-tint)}
+.st-pill[data-state="reading"] .st-pill__dot{background:var(--st-info);box-shadow:0 0 0 4px var(--st-info-tint)}
+.st-pill[data-state="queued"] .st-pill__dot{background:var(--st-warn)}
+
+/* tabs */
+.st-tabs{display:flex;gap:4px;padding:4px;background:var(--st-surface-2);border:1px solid var(--st-line);border-radius:var(--st-r-md)}
+.st-tab{display:inline-flex;align-items:center;gap:8px;height:36px;padding:0 14px;border:0;border-radius:10px;background:transparent;color:var(--st-ink-muted);font:500 var(--st-fs-md)/1 var(--st-font);cursor:pointer}
+.st-tab:hover{color:var(--st-ink)}
+.st-tab[aria-selected="true"]{background:var(--st-surface);color:var(--st-ink);font-weight:700;box-shadow:var(--st-sh-card)}
+
+/* card + metric */
+.st-card{background:var(--st-surface);border:1px solid var(--st-line);border-radius:var(--st-r-lg);box-shadow:var(--st-sh-card);padding:var(--st-s-5)}
+.st-metric{display:flex;flex-direction:column;gap:10px}
+.st-metric__label{display:flex;align-items:center;gap:8px;font-size:var(--st-fs-sm);font-weight:500;color:var(--st-ink-muted)}
+.st-metric__value{font-weight:900;font-size:var(--st-fs-stat);line-height:1;letter-spacing:-.01em;font-variant-numeric:tabular-nums}
+.st-metric__unit{font-weight:500;font-size:var(--st-fs-md);color:var(--st-ink-muted);margin-left:4px}
+.st-metric__spark{width:100%;height:32px;color:var(--st-accent)}
+
+/* progress + gauge */
+.st-progress{height:8px;border-radius:var(--st-r-pill);background:var(--st-surface-2);border:1px solid var(--st-line-soft);overflow:hidden}
+.st-progress__bar{height:100%;border-radius:inherit;background:var(--st-accent);transition:width var(--st-dur) var(--st-ease)}
+.st-progress[data-tone="info"] .st-progress__bar{background:var(--st-info)}
+.st-progress[data-tone="warn"] .st-progress__bar{background:var(--st-warn)}
+.st-progress[data-tone="danger"] .st-progress__bar{background:var(--st-danger)}
+/* gauge: see README for SVG markup (stroke-dasharray on a 270° arc) */
+.st-gauge{position:relative;width:120px;height:120px}
+.st-gauge__track{stroke:var(--st-surface-2)} .st-gauge__fill{stroke:var(--st-accent)}
+
+/* table */
+.st-table{width:100%;border-collapse:collapse;font-size:var(--st-fs-md);font-variant-numeric:tabular-nums}
+.st-table th{text-align:left;font-weight:500;font-size:var(--st-fs-xs);letter-spacing:.04em;text-transform:uppercase;color:var(--st-ink-muted);padding:10px 12px;border-bottom:1px solid var(--st-line)}
+.st-table td{padding:12px;border-bottom:1px solid var(--st-line-soft);color:var(--st-ink-soft)}
+.st-table tr:hover td{background:var(--st-surface-2)}
+.st-table .num{text-align:right}
+
+/* badges */
+.st-badge{display:inline-flex;align-items:center;gap:6px;height:24px;padding:0 10px;border-radius:var(--st-r-pill);font-size:var(--st-fs-xs);font-weight:700;background:var(--st-surface-2);color:var(--st-ink-muted);border:1px solid var(--st-line)}
+.st-badge::before{content:"";width:6px;height:6px;border-radius:50%;background:currentColor}
+.st-badge--reading{background:var(--st-info-tint);color:var(--st-info-text);border-color:transparent}
+.st-badge--generating{background:var(--st-accent-tint);color:var(--st-accent-text);border-color:transparent}
+.st-badge--queued{background:var(--st-warn-tint);color:var(--st-warn-text);border-color:transparent}
+.st-badge--error{background:var(--st-danger-tint);color:var(--st-danger-text);border-color:transparent}
+
+/* chat */
+.st-msg{display:flex;flex-direction:column;gap:6px;max-width:720px}
+.st-msg--user{align-self:flex-end;align-items:flex-end}
+.st-bubble{padding:12px 16px;border-radius:var(--st-r-lg);font-size:var(--st-fs-base);line-height:1.55;color:var(--st-ink-soft)}
+.st-msg--user .st-bubble{background:var(--st-accent);color:var(--st-accent-ink);border-bottom-right-radius:6px}
+.st-msg--assistant .st-bubble{background:var(--st-surface);border:1px solid var(--st-line);border-bottom-left-radius:6px}
+.st-msg__meta{font-size:var(--st-fs-xs);color:var(--st-ink-muted);display:flex;gap:8px;align-items:center}
+
+/* collapsible — <details class="st-collapse"><summary>…</summary><div class="st-collapse__body">…</div></details> */
+.st-collapse{border:1px solid var(--st-line);border-radius:var(--st-r-md);background:var(--st-surface-2)}
+.st-collapse>summary{list-style:none;display:flex;align-items:center;gap:8px;padding:10px 14px;cursor:pointer;font-size:var(--st-fs-sm);font-weight:500;color:var(--st-ink-muted)}
+.st-collapse>summary::-webkit-details-marker{display:none}
+.st-collapse>summary .st-chev{margin-left:auto;transition:transform var(--st-dur) var(--st-ease)}
+.st-collapse[open]>summary .st-chev{transform:rotate(180deg)}
+.st-collapse__body{padding:0 14px 14px;font-size:var(--st-fs-sm);color:var(--st-ink-muted);line-height:1.6}
+
+/* code */
+.st-code{border-radius:var(--st-r-md);background:var(--st-code-bg);color:var(--st-code-ink);overflow:hidden}
+.st-code__head{display:flex;align-items:center;justify-content:space-between;padding:8px 8px 8px 14px;font:500 var(--st-fs-xs)/1 var(--st-font);color:#9aa3ab;border-bottom:1px solid rgba(255,255,255,.06)}
+.st-code pre{margin:0;padding:14px;overflow:auto;font:400 13px/1.6 var(--st-font-mono)}
+.st-code .st-btn--icon{color:#9aa3ab} .st-code .st-btn--icon:hover{background:rgba(255,255,255,.08);color:#fff}
+
+/* composer */
+.st-composer{display:flex;flex-direction:column;gap:8px;padding:10px;background:var(--st-surface);border:1px solid var(--st-line);border-radius:var(--st-r-lg);box-shadow:var(--st-sh-bar)}
+.st-composer:focus-within{border-color:var(--st-focus)}
+.st-composer textarea{border:0;outline:0;resize:none;background:transparent;color:var(--st-ink);font:400 var(--st-fs-base)/1.5 var(--st-font);padding:6px 8px;min-height:48px}
+.st-composer__bar{display:flex;align-items:center;gap:6px}
+.st-composer__bar .spacer{flex:1}
+
+/* drawer */
+.st-drawer{position:fixed;top:0;right:0;bottom:0;width:360px;max-width:100vw;background:var(--st-surface);border-left:1px solid var(--st-line);box-shadow:var(--st-sh-pop);display:flex;flex-direction:column;transform:translateX(100%);transition:transform 220ms var(--st-ease)}
+.st-drawer[data-open="true"]{transform:none}
+.st-drawer__head{display:flex;align-items:center;justify-content:space-between;padding:18px 20px;border-bottom:1px solid var(--st-line)}
+.st-drawer__body{padding:20px;display:flex;flex-direction:column;gap:22px;overflow:auto}
+
+/* buttons */
+.st-btn{display:inline-flex;align-items:center;justify-content:center;gap:8px;height:42px;padding:0 18px;border-radius:var(--st-r-md);border:0;font:700 var(--st-fs-md)/1 var(--st-font);cursor:pointer;transition:background var(--st-dur),transform 120ms}
+.st-btn:active{transform:translateY(1px)}
+.st-btn--primary{background:var(--st-accent);color:var(--st-accent-ink);box-shadow:0 6px 16px rgba(16,185,129,.24),inset 0 1px 0 rgba(255,255,255,.18)}
+.st-btn--primary:hover{background:var(--st-accent-hover)}
+.st-btn--secondary{background:transparent;color:var(--st-ink);border:1.5px solid var(--st-line)}
+.st-btn--secondary:hover{background:var(--st-surface-2)}
+.st-btn--danger{background:var(--st-danger-tint);color:var(--st-danger-text)}
+.st-btn--icon{width:36px;height:36px;padding:0;border-radius:10px;background:transparent;color:var(--st-ink-muted)}
+.st-btn--icon:hover{background:var(--st-surface-2);color:var(--st-ink)}
+.st-btn:disabled{opacity:.45;cursor:not-allowed}
+
+/* form controls */
+.st-field{display:flex;flex-direction:column;gap:8px}
+.st-label{display:flex;justify-content:space-between;font-size:var(--st-fs-sm);font-weight:500;color:var(--st-ink-soft)}
+.st-label output{font-variant-numeric:tabular-nums;color:var(--st-ink-muted)}
+.st-input{height:44px;padding:0 14px;border-radius:var(--st-r-md);border:1.5px solid var(--st-line);background:var(--st-surface);color:var(--st-ink);font:400 var(--st-fs-md) var(--st-font)}
+.st-input:focus{outline:0;border-color:var(--st-focus);box-shadow:0 0 0 3px color-mix(in srgb,var(--st-focus) 20%,transparent)}
+.st-slider{width:100%;accent-color:var(--st-accent)}
+.st-toggle{position:relative;width:40px;height:24px;border-radius:var(--st-r-pill);border:0;background:var(--st-line);cursor:pointer;transition:background var(--st-dur)}
+.st-toggle::after{content:"";position:absolute;top:3px;left:3px;width:18px;height:18px;border-radius:50%;background:#fff;box-shadow:0 1px 2px rgba(0,0,0,.2);transition:transform var(--st-dur) var(--st-ease)}
+.st-toggle[aria-checked="true"]{background:var(--st-accent)}
+.st-toggle[aria-checked="true"]::after{transform:translateX(16px)}
+
+/* toast / alert */
+.st-toast{display:flex;align-items:flex-start;gap:12px;padding:14px 16px;border-radius:var(--st-r-md);background:var(--st-surface);border:1px solid var(--st-line);box-shadow:var(--st-sh-pop);font-size:var(--st-fs-md);color:var(--st-ink-soft);max-width:420px}
+.st-toast__title{font-weight:700;color:var(--st-ink)}
+.st-toast--info .st-icon{color:var(--st-info)}
+.st-toast--success .st-icon{color:var(--st-accent)}
+.st-toast--warn .st-icon{color:var(--st-warn)}
+.st-toast--error .st-icon{color:var(--st-danger)}
+.st-toasts{position:fixed;right:20px;bottom:20px;display:flex;flex-direction:column;gap:10px;z-index:50}
@@ -0,0 +1,93 @@
+Copyright 2021 The Outfit Project Authors (https://github.com/Outfitio/Outfit-Fonts)
+
+This Font Software is licensed under the SIL Open Font License, Version 1.1.
+This license is copied below, and is also available with a FAQ at:
+https://scripts.sil.org/OFL
+
+
+-----------------------------------------------------------
+SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007
+-----------------------------------------------------------
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide
+development of collaborative font projects, to support the font creation
+efforts of academic and linguistic communities, and to provide a free and
+open framework in which fonts may be shared and improved in partnership
+with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and
+redistributed freely as long as they are not sold by themselves. The
+fonts, including any derivative works, can be bundled, embedded, 
+redistributed and/or sold with any software provided that any reserved
+names are not used by derivative works. The fonts and derivatives,
+however, cannot be released under any other type of license. The
+requirement for fonts to remain under this license does not apply
+to any document created using the fonts or their derivatives.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright
+Holder(s) under this license and clearly marked as such. This may
+include source files, build scripts and documentation.
+
+"Reserved Font Name" refers to any names specified as such after the
+copyright statement(s).
+
+"Original Version" refers to the collection of Font Software components as
+distributed by the Copyright Holder(s).
+
+"Modified Version" refers to any derivative made by adding to, deleting,
+or substituting -- in part or in whole -- any of the components of the
+Original Version, by changing formats or by porting the Font Software to a
+new environment.
+
+"Author" refers to any designer, engineer, programmer, technical
+writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of the Font Software, to use, study, copy, merge, embed, modify,
+redistribute, and sell modified and unmodified copies of the Font
+Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components,
+in Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled,
+redistributed and/or sold with any software, provided that each copy
+contains the above copyright notice and this license. These can be
+included either as stand-alone text files, human-readable headers or
+in the appropriate machine-readable metadata fields within text or
+binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font
+Name(s) unless explicit written permission is granted by the corresponding
+Copyright Holder. This restriction only applies to the primary font name as
+presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font
+Software shall not be used to promote, endorse or advertise any
+Modified Version, except to acknowledge the contribution(s) of the
+Copyright Holder(s) and the Author(s) or with their explicit written
+permission.
+
+5) The Font Software, modified or unmodified, in part or in whole,
+must be distributed entirely under this license, and must not be
+distributed under any other license. The requirement for fonts to
+remain under this license does not apply to any document created
+using the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are
+not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
+OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE
+COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
+DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM
+OTHER DEALINGS IN THE FONT SOFTWARE.
@@ -0,0 +1,211 @@
+<!doctype html>
+<html lang="en" data-theme="light">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>Strata</title>
+<link rel="stylesheet" href="/web/tokens.css">
+<link rel="stylesheet" href="/web/components.css">
+<link rel="stylesheet" href="/web/app.css">
+<script>
+  // the theme before the first paint: the saved choice, else the system's
+  try {
+    const t = localStorage.getItem("strata.theme") ||
+      (matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light");
+    document.documentElement.dataset.theme = t;
+  } catch (e) {}
+</script>
+</head>
+<body>
+<header class="st-header">
+  <span class="st-header__brand">Strata</span>
+  <nav class="st-tabs" role="tablist" aria-label="Views">
+    <button class="st-tab" role="tab" id="tab-btn-chat" aria-controls="view-chat" aria-selected="true" data-tab="chat">
+      <svg class="st-icon st-icon--sm"><use href="/web/sprite.svg#i-chat"/></svg>Chat</button>
+    <button class="st-tab" role="tab" id="tab-btn-monitor" aria-controls="view-monitor" aria-selected="false" data-tab="monitor">
+      <svg class="st-icon st-icon--sm"><use href="/web/sprite.svg#i-activity"/></svg>Monitor</button>
+    <button class="st-tab" role="tab" id="tab-btn-about" aria-controls="view-about" aria-selected="false" data-tab="about">
+      <svg class="st-icon st-icon--sm"><use href="/web/sprite.svg#i-info"/></svg>About</button>
+  </nav>
+  <span class="st-header__spacer"></span>
+  <span class="st-pill" id="pill" data-state="idle" role="status" aria-live="polite">
+    <span class="st-pill__dot"></span><span id="pill-text">Connecting&hellip;</span></span>
+  <button class="st-btn st-btn--icon" id="theme-btn" title="Light / dark" aria-label="Switch between light and dark">
+    <svg class="st-icon"><use id="theme-icon" href="/web/sprite.svg#i-moon"/></svg></button>
+</header>
+
+<main>
+  <!-- ============================ Chat ============================ -->
+  <section class="view view--chat" id="view-chat" role="tabpanel" aria-labelledby="tab-btn-chat">
+    <div class="chat-scroll" id="chat-scroll">
+      <div class="chat" id="chat">
+        <div class="chat-empty" id="chat-empty">
+          <div class="chat-empty__title">Ask anything</div>
+          <div class="chat-empty__sub" id="chat-empty-sub">The model runs on this PC. Nothing leaves it.</div>
+        </div>
+      </div>
+    </div>
+    <div class="composer-wrap">
+      <form class="st-composer" id="composer">
+        <div class="attachments" id="attachments" hidden></div>
+        <textarea id="input" rows="2" placeholder="Ask anything&hellip;" aria-label="Message"></textarea>
+        <div class="st-composer__bar">
+          <button type="button" class="st-btn st-btn--icon" id="attach-btn" title="Attach a picture" aria-label="Attach a picture" hidden>
+            <svg class="st-icon"><use href="/web/sprite.svg#i-attach"/></svg></button>
+          <input type="file" id="file" accept="image/*" multiple hidden>
+          <button type="button" class="st-btn st-btn--icon" id="new-btn" title="New chat" aria-label="New chat">
+            <svg class="st-icon"><use href="/web/sprite.svg#i-new-chat"/></svg></button>
+          <button type="button" class="st-btn st-btn--icon" id="export-btn" title="Save this chat as Markdown" aria-label="Save this chat">
+            <svg class="st-icon"><use href="/web/sprite.svg#i-download"/></svg></button>
+          <button type="button" class="st-btn st-btn--icon" id="sampling-btn" title="Sampling and thinking" aria-label="Sampling and thinking">
+            <svg class="st-icon"><use href="/web/sprite.svg#i-settings"/></svg></button>
+          <span class="spacer"></span>
+          <span class="composer-hint" id="composer-hint"></span>
+          <button type="button" class="st-btn st-btn--secondary" id="stop-btn" hidden>
+            <svg class="st-icon st-icon--sm"><use href="/web/sprite.svg#i-stop"/></svg>Stop</button>
+          <button type="submit" class="st-btn st-btn--primary st-btn--send" id="send-btn" aria-label="Send">
+            <svg class="st-icon"><use href="/web/sprite.svg#i-send"/></svg></button>
+        </div>
+      </form>
+    </div>
+  </section>
+
+  <!-- ============================ Monitor ============================ -->
+  <section class="view view--monitor" id="view-monitor" role="tabpanel" aria-labelledby="tab-btn-monitor" hidden>
+    <div class="monitor">
+      <div class="st-card state-card">
+        <div class="state-card__head">
+          <span class="card-title">Model state</span>
+          <span class="badges" id="state-badges">
+            <span class="st-badge" data-s="idle">Idle</span>
+            <span class="st-badge st-badge--reading" data-s="reading">Reading</span>
+            <span class="st-badge st-badge--generating" data-s="generating">Generating</span>
+            <span class="st-badge st-badge--queued" data-s="queued">Queued</span>
+            <span class="st-badge st-badge--error" data-s="error">Error</span>
+          </span>
+        </div>
+        <div class="state-card__row"><span id="state-label">Waiting for a request</span><span class="muted" id="state-detail"></span></div>
+        <div class="st-progress" id="state-progress"><div class="st-progress__bar" id="state-bar" style="width:0%"></div></div>
+      </div>
+
+      <div class="metrics" id="metrics">
+        <!-- filled by app.js: Speed, GPU load, VRAM, GPU temp, Power, PCIe, CPU, Disk read -->
+      </div>
+
+      <div class="monitor-row">
+        <div class="st-card ctx-card">
+          <span class="card-title">Context fill</span>
+          <div class="ctx-gauge">
+            <svg class="st-gauge" viewBox="0 0 120 120" aria-hidden="true">
+              <circle class="st-gauge__track" cx="60" cy="60" r="50" fill="none" stroke-width="10" stroke-linecap="round"
+                      stroke-dasharray="235.6 314.2" transform="rotate(135 60 60)"/>
+              <circle class="st-gauge__fill" id="ctx-fill" cx="60" cy="60" r="50" fill="none" stroke-width="10"
+                      stroke-linecap="round" stroke-dasharray="0 314.2" transform="rotate(135 60 60)"/>
+            </svg>
+            <div class="ctx-gauge__label"><span class="ctx-gauge__pct" id="ctx-pct">0%</span><span class="muted" id="ctx-sub">&ndash;</span></div>
+          </div>
+          <div class="bar-row"><span>Experts in VRAM</span><span class="muted" id="slots-text">&ndash;</span></div>
+          <div class="st-progress"><div class="st-progress__bar" id="slots-bar" style="width:0%"></div></div>
+          <div class="bar-row"><span>System RAM</span><span class="muted" id="ram-text">&ndash;</span></div>
+          <div class="st-progress" id="ram-progress"><div class="st-progress__bar" id="ram-bar" style="width:0%"></div></div>
+          <div class="bar-row"><span>GPU temperature</span><span class="muted" id="temp-text">&ndash;</span></div>
+          <div class="st-progress" data-tone="warn"><div class="st-progress__bar" id="temp-bar" style="width:0%"></div></div>
+        </div>
+        <div class="st-card req-card">
+          <span class="card-title">Recent requests</span>
+          <div class="table-wrap">
+            <table class="st-table">
+              <thead><tr><th>Time</th><th>Status</th><th class="num">Prompt</th><th class="num">Reused</th><th class="num">Output</th>
+                <th class="num">Tok/s</th><th class="num">Duration</th></tr></thead>
+              <tbody id="req-body"><tr><td colspan="7" class="muted">No requests yet</td></tr></tbody>
+            </table>
+          </div>
+        </div>
+      </div>
+    </div>
+  </section>
+
+  <!-- ============================ About ============================ -->
+  <section class="view view--about" id="view-about" role="tabpanel" aria-labelledby="tab-btn-about" hidden>
+    <div class="about">
+      <div class="st-card">
+        <span class="card-title">Model and engine</span>
+        <dl class="facts" id="facts-engine"></dl>
+      </div>
+      <div class="st-card">
+        <span class="card-title">This PC</span>
+        <dl class="facts" id="facts-hw"></dl>
+      </div>
+      <div class="st-card">
+        <span class="card-title">Connect your tools</span>
+        <p class="muted">Any OpenAI- or Anthropic-compatible client works with these addresses.</p>
+        <dl class="facts" id="facts-api"></dl>
+      </div>
+      <div class="st-card">
+        <span class="card-title">Settings</span>
+        <div class="st-field">
+          <label class="st-label" for="api-key">API key <output>only if the server was started with one</output></label>
+          <input class="st-input" id="api-key" type="password" autocomplete="off" placeholder="Not needed">
+        </div>
+        <div class="toggle-row"><span>Dark theme</span>
+          <button class="st-toggle" id="dark-toggle" role="switch" aria-checked="false" aria-label="Dark theme"></button></div>
+        <p class="muted small">Chats, settings and the key are kept in this browser only.
+          <a href="https://github.com/Niko1221/Strata" target="_blank" rel="noopener noreferrer">Strata on GitHub</a></p>
+      </div>
+    </div>
+  </section>
+</main>
+
+<!-- ============================ Sampling drawer ============================ -->
+<aside class="st-drawer" id="drawer" data-open="false" aria-label="Sampling and thinking" aria-hidden="true">
+  <div class="st-drawer__head"><span class="card-title">Sampling</span>
+    <button class="st-btn st-btn--icon" id="drawer-close" aria-label="Close">
+      <svg class="st-icon"><use href="/web/sprite.svg#i-chevron"/></svg></button></div>
+  <div class="st-drawer__body">
+    <div class="st-field">
+      <label class="st-label" for="s-thinking">Thinking <output id="o-thinking"></output></label>
+      <div class="seg" id="s-thinking" role="radiogroup" aria-label="Thinking">
+        <button type="button" role="radio" data-v="none">Off</button><button type="button" role="radio" data-v="low">Low</button>
+        <button type="button" role="radio" data-v="medium">Medium</button><button type="button" role="radio" data-v="high">High</button>
+      </div>
+    </div>
+    <div class="st-field">
+      <label class="st-label" for="s-temp">Temperature <output id="o-temp"></output></label>
+      <input class="st-slider" type="range" id="s-temp" min="0" max="1.5" step="0.05">
+      <span class="muted small">0 = always the most likely word (exact, repeatable)</span>
+    </div>
+    <div class="st-field">
+      <label class="st-label" for="s-topp">Top-p <output id="o-topp"></output></label>
+      <input class="st-slider" type="range" id="s-topp" min="0.05" max="1" step="0.05">
+    </div>
+    <div class="st-field">
+      <label class="st-label" for="s-topk">Top-k <output id="o-topk"></output></label>
+      <input class="st-slider" type="range" id="s-topk" min="1" max="64" step="1">
+    </div>
+    <div class="st-field">
+      <label class="st-label" for="s-max">Max tokens <output>empty = until done</output></label>
+      <input class="st-input" id="s-max" type="number" min="1" step="1" placeholder="Until done">
+    </div>
+    <div class="st-field">
+      <label class="st-label" for="s-seed">Seed <output>empty = random</output></label>
+      <input class="st-input" id="s-seed" type="number" min="1" step="1" placeholder="Random">
+    </div>
+    <div class="toggle-row"><span>Show thinking<br><span class="muted small">expanded while it streams</span></span>
+      <button class="st-toggle" id="s-show" role="switch" aria-checked="true" aria-label="Show thinking"></button></div>
+    <div class="toggle-row" id="esp-row" hidden><span>Experimental speed projection<br><span class="muted small">the
+      engine's control vector; off = the stock model. Switching reads the chat again once</span></span>
+      <button class="st-toggle" id="s-esp" role="switch" aria-checked="true" aria-label="Experimental speed projection"></button></div>
+    <div class="toggle-row"><span>Use for other apps too<br><span class="muted small">omp and other API clients get
+      these settings for anything they don't set themselves</span></span>
+      <button class="st-toggle" id="s-share" role="switch" aria-checked="false" aria-label="Use for other apps too"></button></div>
+    <div class="drawer-actions">
+      <button class="st-btn st-btn--secondary" id="s-reset" type="button">Reset</button>
+      <button class="st-btn st-btn--primary" id="s-apply" type="button">Apply</button>
+    </div>
+  </div>
+</aside>
+<div class="scrim" id="scrim" hidden></div>
+<div class="st-toasts" id="toasts" aria-live="polite"></div>
+<script src="/web/app.js"></script>
+</body>
+</html>
@@ -0,0 +1,34 @@
+<svg xmlns="http://www.w3.org/2000/svg" style="display:none" xmlns:c2pa="http://c2pa.org/manifest"><metadata><c2pa:manifest>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 truncated
+<symbol id="i-chat" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M21 11.5C21 16.194 16.971 20 12 20C10.8 20 9.65 19.78 8.6 19.37L4 20.5L5.2 16.8C3.83 15.34 3 13.5 3 11.5C3 6.806 7.029 3 12 3C16.971 3 21 6.806 21 11.5Z" fill="currentColor"></path><path d="M21 11.5C21 16.194 16.971 20 12 20C10.8 20 9.65 19.78 8.6 19.37L4 20.5L5.2 16.8C3.83 15.34 3 13.5 3 11.5C3 6.806 7.029 3 12 3C16.971 3 21 6.806 21 11.5Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M8 11.5H8.01M12 11.5H12.01M16 11.5H16.01" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-activity" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M2 12C2 7.286 2 4.929 3.464 3.464C4.929 2 7.286 2 12 2C16.714 2 19.071 2 20.536 3.464C22 4.929 22 7.286 22 12C22 16.714 22 19.071 20.536 20.536C19.071 22 16.714 22 12 22C7.286 22 4.929 22 3.464 20.536C2 19.071 2 16.714 2 12Z" fill="currentColor"></path><path d="M2 12C2 7.286 2 4.929 3.464 3.464C4.929 2 7.286 2 12 2C16.714 2 19.071 2 20.536 3.464C22 4.929 22 7.286 22 12C22 16.714 22 19.071 20.536 20.536C19.071 22 16.714 22 12 22C7.286 22 4.929 22 3.464 20.536C2 19.071 2 16.714 2 12Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M6 12.5H8.5L10.5 8L13.5 16L15.5 11.5H18" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-settings" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M12.22 2H11.78A2 2 0 0 0 9.78 4V4.18A2 2 0 0 1 8.78 5.91L8.35 6.16A2 2 0 0 1 6.35 6.16L6.2 6.08A2 2 0 0 0 3.47 6.81L3.25 7.19A2 2 0 0 0 3.98 9.92L4.13 10.02A2 2 0 0 1 5.13 11.74V12.25A2 2 0 0 1 4.13 13.99L3.98 14.08A2 2 0 0 0 3.25 16.81L3.47 17.19A2 2 0 0 0 6.2 17.92L6.35 17.84A2 2 0 0 1 8.35 17.84L8.78 18.09A2 2 0 0 1 9.78 19.82V20A2 2 0 0 0 11.78 22H12.22A2 2 0 0 0 14.22 20V19.82A2 2 0 0 1 15.22 18.09L15.65 17.84A2 2 0 0 1 17.65 17.84L17.8 17.92A2 2 0 0 0 20.53 17.19L20.75 16.8A2 2 0 0 0 20.02 14.07L19.87 13.99A2 2 0 0 1 18.87 12.25V11.75A2 2 0 0 1 19.87 10.01L20.02 9.92A2 2 0 0 0 20.75 7.19L20.53 6.81A2 2 0 0 0 17.8 6.08L17.65 6.16A2 2 0 0 1 15.65 6.16L15.22 5.91A2 2 0 0 1 14.22 4.18V4A2 2 0 0 0 12.22 2Z" fill="currentColor"></path><path d="M12.22 2H11.78A2 2 0 0 0 9.78 4V4.18A2 2 0 0 1 8.78 5.91L8.35 6.16A2 2 0 0 1 6.35 6.16L6.2 6.08A2 2 0 0 0 3.47 6.81L3.25 7.19A2 2 0 0 0 3.98 9.92L4.13 10.02A2 2 0 0 1 5.13 11.74V12.25A2 2 0 0 1 4.13 13.99L3.98 14.08A2 2 0 0 0 3.25 16.81L3.47 17.19A2 2 0 0 0 6.2 17.92L6.35 17.84A2 2 0 0 1 8.35 17.84L8.78 18.09A2 2 0 0 1 9.78 19.82V20A2 2 0 0 0 11.78 22H12.22A2 2 0 0 0 14.22 20V19.82A2 2 0 0 1 15.22 18.09L15.65 17.84A2 2 0 0 1 17.65 17.84L17.8 17.92A2 2 0 0 0 20.53 17.19L20.75 16.8A2 2 0 0 0 20.02 14.07L19.87 13.99A2 2 0 0 1 18.87 12.25V11.75A2 2 0 0 1 19.87 10.01L20.02 9.92A2 2 0 0 0 20.75 7.19L20.53 6.81A2 2 0 0 0 17.8 6.08L17.65 6.16A2 2 0 0 1 15.65 6.16L15.22 5.91A2 2 0 0 1 14.22 4.18V4A2 2 0 0 0 12.22 2Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M15 12C15 13.6569 13.6569 15 12 15C10.3431 15 9 13.6569 9 12C9 10.3431 10.3431 9 12 9C13.6569 9 15 10.3431 15 12Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-gpu" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M2 7.5C2 6.12 3.12 5 4.5 5H19.5C20.88 5 22 6.12 22 7.5V15.5C22 16.88 20.88 18 19.5 18H4.5C3.12 18 2 16.88 2 15.5V7.5Z" fill="currentColor"></path><path d="M2 7.5C2 6.12 3.12 5 4.5 5H19.5C20.88 5 22 6.12 22 7.5V15.5C22 16.88 20.88 18 19.5 18H4.5C3.12 18 2 16.88 2 15.5V7.5Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M12.5 11.5C12.5 13.433 10.933 15 9 15C7.067 15 5.5 13.433 5.5 11.5C5.5 9.567 7.067 8 9 8C10.933 8 12.5 9.567 12.5 11.5Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M16 9V14M19 9V14" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M6 18V20.5M10 18V20.5M14 18V20.5" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-cpu" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M6 9C6 7.343 7.343 6 9 6H15C16.657 6 18 7.343 18 9V15C18 16.657 16.657 18 15 18H9C7.343 18 6 16.657 6 15V9Z" fill="currentColor"></path><path d="M6 9C6 7.343 7.343 6 9 6H15C16.657 6 18 7.343 18 9V15C18 16.657 16.657 18 15 18H9C7.343 18 6 16.657 6 15V9Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M10 11C10 10.448 10.448 10 11 10H13C13.552 10 14 10.448 14 11V13C14 13.552 13.552 14 13 14H11C10.448 14 10 13.552 10 13V11Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M9.5 2.5V6M14.5 2.5V6M9.5 18V21.5M14.5 18V21.5M2.5 9.5H6M2.5 14.5H6M18 9.5H21.5M18 14.5H21.5" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-memory" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M2 8C2 6.895 2.895 6 4 6H20C21.105 6 22 6.895 22 8V15H2V8Z" fill="currentColor"></path><path d="M2 8C2 6.895 2.895 6 4 6H20C21.105 6 22 6.895 22 8V15H2V8Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M6 9.5V11.5M10 9.5V11.5M14 9.5V11.5M18 9.5V11.5" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M4 15V18M8 15V18M12 15V18M16 15V18M20 15V18" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-disk" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M2 9C2 7.343 3.343 6 5 6H19C20.657 6 22 7.343 22 9V15C22 16.657 20.657 18 19 18H5C3.343 18 2 16.657 2 15V9Z" fill="currentColor"></path><path d="M2 9C2 7.343 3.343 6 5 6H19C20.657 6 22 7.343 22 9V15C22 16.657 20.657 18 19 18H5C3.343 18 2 16.657 2 15V9Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M6 12H12.5" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M17.5 12H17.51" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-thermometer" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M14 14.76V5C14 3.895 13.105 3 12 3C10.895 3 10 3.895 10 5V14.76C8.8 15.45 8 16.73 8 18.2C8 20.4 9.8 22 12 22C14.2 22 16 20.4 16 18.2C16 16.73 15.2 15.45 14 14.76Z" fill="currentColor"></path><path d="M14 14.76V5C14 3.895 13.105 3 12 3C10.895 3 10 3.895 10 5V14.76C8.8 15.45 8 16.73 8 18.2C8 20.4 9.8 22 12 22C14.2 22 16 20.4 16 18.2C16 16.73 15.2 15.45 14 14.76Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M12 17.5V9" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-bolt" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M13 2L4 14H11L10 22L20 9.5H13L13 2Z" fill="currentColor"></path><path d="M13 2L4 14H11L10 22L20 9.5H13L13 2Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-link" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M8.46 20.06L14.26 14.26A3.2 3.2 0 0 0 9.74 9.74L3.94 15.54A3.2 3.2 0 0 0 8.46 20.06Z" fill="currentColor"></path><path d="M10 13C10.43 13.57 10.98 14.05 11.61 14.39C12.24 14.74 12.93 14.94 13.65 14.99C14.36 15.04 15.08 14.94 15.75 14.69C16.42 14.44 17.03 14.05 17.54 13.54L20.54 10.54C21.45 9.6 21.95 8.33 21.94 7.02C21.93 5.71 21.41 4.46 20.48 3.53C19.55 2.6 18.3 2.08 16.99 2.07C15.68 2.06 14.41 2.56 13.47 3.47L11.75 5.18" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M14 11C13.57 10.43 13.02 9.95 12.39 9.61C11.76 9.26 11.07 9.06 10.35 9.01C9.64 8.96 8.92 9.06 8.25 9.31C7.58 9.56 6.97 9.95 6.46 10.46L3.46 13.46C2.55 14.4 2.05 15.67 2.06 16.98C2.07 18.29 2.59 19.54 3.52 20.47C4.45 21.4 5.7 21.92 7.01 21.93C8.32 21.94 9.59 21.44 10.53 20.53L12.24 18.82" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-clock" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M22 12C22 17.5228 17.5228 22 12 22C6.47715 22 2 17.5228 2 12C2 6.47715 6.47715 2 12 2C17.5228 2 22 6.47715 22 12Z" fill="currentColor"></path><path d="M22 12C22 17.5228 17.5228 22 12 22C6.47715 22 2 17.5228 2 12C2 6.47715 6.47715 2 12 2C17.5228 2 22 6.47715 22 12Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M12 7V12L15 14" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-gauge" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M2 15C2 9.477 6.477 5 12 5C17.523 5 22 9.477 22 15C22 16.5 21.7 17.8 21.1 19H2.9C2.3 17.8 2 16.5 2 15Z" fill="currentColor"></path><path d="M2 15C2 9.477 6.477 5 12 5C17.523 5 22 9.477 22 15C22 16.5 21.7 17.8 21.1 19H2.9C2.3 17.8 2 16.5 2 15Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M12 15L16 10.5" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M13 15C13 15.552 12.552 16 12 16C11.448 16 11 15.552 11 15C11 14.448 11.448 14 12 14C12.552 14 13 14.448 13 15Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-layers" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M12 3L22 8L12 13L2 8L12 3Z" fill="currentColor"></path><path d="M12 3L22 8L12 13L2 8L12 3Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M2 12L12 17L22 12" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M2 16L12 21L22 16" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-experts" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M3 5.5C3 4.119 4.119 3 5.5 3H6.5C7.881 3 9 4.119 9 5.5V6.5C9 7.881 7.881 9 6.5 9H5.5C4.119 9 3 7.881 3 6.5V5.5Z" fill="currentColor"></path><path opacity="0.12" d="M15 17.5C15 16.119 16.119 15 17.5 15H18.5C19.881 15 21 16.119 21 17.5V18.5C21 19.881 19.881 21 18.5 21H17.5C16.119 21 15 19.881 15 18.5V17.5Z" fill="currentColor"></path><path d="M3 5.5C3 4.119 4.119 3 5.5 3H6.5C7.881 3 9 4.119 9 5.5V6.5C9 7.881 7.881 9 6.5 9H5.5C4.119 9 3 7.881 3 6.5V5.5Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M15 5.5C15 4.119 16.119 3 17.5 3H18.5C19.881 3 21 4.119 21 5.5V6.5C21 7.881 19.881 9 18.5 9H17.5C16.119 9 15 7.881 15 6.5V5.5Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M3 17.5C3 16.119 4.119 15 5.5 15H6.5C7.881 15 9 16.119 9 17.5V18.5C9 19.881 7.881 21 6.5 21H5.5C4.119 21 3 19.881 3 18.5V17.5Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M15 17.5C15 16.119 16.119 15 17.5 15H18.5C19.881 15 21 16.119 21 17.5V18.5C21 19.881 19.881 21 18.5 21H17.5C16.119 21 15 19.881 15 18.5V17.5Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-send" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M21 3L2 10L10 13.5L13.5 21L21 3Z" fill="currentColor"></path><path d="M21 3L2 10L10 13.5L13.5 21L21 3Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M10 13.5L21 3" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-stop" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M5 8C5 6.343 6.343 5 8 5H16C17.657 5 19 6.343 19 8V16C19 17.657 17.657 19 16 19H8C6.343 19 5 17.657 5 16V8Z" fill="currentColor"></path><path d="M5 8C5 6.343 6.343 5 8 5H16C17.657 5 19 6.343 19 8V16C19 17.657 17.657 19 16 19H8C6.343 19 5 17.657 5 16V8Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-attach" viewBox="0 0 24 24" fill="none"><path d="M21.44 11.05L12.25 20.24A6 6 0 0 1 3.76 11.75L12.33 3.18A4 4 0 1 1 18 8.84L9.41 17.41A2 2 0 0 1 6.58 14.58L15.07 6.1" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-image" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M3 7C3 4.791 4.791 3 7 3H17C19.209 3 21 4.791 21 7V17C21 19.209 19.209 21 17 21H7C4.791 21 3 19.209 3 17V7Z" fill="currentColor"></path><path d="M3 7C3 4.791 4.791 3 7 3H17C19.209 3 21 4.791 21 7V17C21 19.209 19.209 21 17 21H7C4.791 21 3 19.209 3 17V7Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M10 8.5C10 9.328 9.328 10 8.5 10C7.672 10 7 9.328 7 8.5C7 7.672 7.672 7 8.5 7C9.328 7 10 7.672 10 8.5Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M3.5 17.5L8 13L11 16L15 12L20.5 17.5" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-copy" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M8 10C8 8.895 8.895 8 10 8H18C19.105 8 20 8.895 20 10V18C20 19.105 19.105 20 18 20H10C8.895 20 8 19.105 8 18V10Z" fill="currentColor"></path><path d="M8 10C8 8.895 8.895 8 10 8H18C19.105 8 20 8.895 20 10V18C20 19.105 19.105 20 18 20H10C8.895 20 8 19.105 8 18V10Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M16 8V6C16 4.895 15.105 4 14 4H6C4.895 4 4 4.895 4 6V14C4 15.105 4.895 16 6 16H8" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-check" viewBox="0 0 24 24" fill="none"><path d="M4.5 12.5L9.5 17.5L19.5 6.5" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-trash" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M5 6L6 19C6.08 20.1 7 21 8.1 21H15.9C17 21 17.92 20.1 18 19L19 6H5Z" fill="currentColor"></path><path d="M5 6L6 19C6.08 20.1 7 21 8.1 21H15.9C17 21 17.92 20.1 18 19L19 6" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M3 6H21" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M9 6V4.5C9 3.672 9.672 3 10.5 3H13.5C14.328 3 15 3.672 15 4.5V6" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M10 10.5V16.5M14 10.5V16.5" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-new-chat" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M21 11.5C21 16.194 16.971 20 12 20C10.8 20 9.65 19.78 8.6 19.37L4 20.5L5.2 16.8C3.83 15.34 3 13.5 3 11.5C3 6.806 7.029 3 12 3C16.971 3 21 6.806 21 11.5Z" fill="currentColor"></path><path d="M21 11.5C21 16.194 16.971 20 12 20C10.8 20 9.65 19.78 8.6 19.37L4 20.5L5.2 16.8C3.83 15.34 3 13.5 3 11.5C3 6.806 7.029 3 12 3C16.971 3 21 6.806 21 11.5Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M12 8.5V14.5M9 11.5H15" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-chevron" viewBox="0 0 24 24" fill="none"><path d="M6 9L12 15L18 9" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-thinking" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M12 2.5C8.41 2.5 5.5 5.41 5.5 9C5.5 11.3 6.7 13.3 8.5 14.5V16.5C8.5 17.05 8.95 17.5 9.5 17.5H14.5C15.05 17.5 15.5 17.05 15.5 16.5V14.5C17.3 13.3 18.5 11.3 18.5 9C18.5 5.41 15.59 2.5 12 2.5Z" fill="currentColor"></path><path d="M12 2.5C8.41 2.5 5.5 5.41 5.5 9C5.5 11.3 6.7 13.3 8.5 14.5V16.5C8.5 17.05 8.95 17.5 9.5 17.5H14.5C15.05 17.5 15.5 17.05 15.5 16.5V14.5C17.3 13.3 18.5 11.3 18.5 9C18.5 5.41 15.59 2.5 12 2.5Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M9.5 20.5H14.5" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-tool" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M14.7 6.3A1 1 0 0 0 14.7 7.7L16.3 9.3A1 1 0 0 0 17.7 9.3L21.47 5.53A6 6 0 0 1 13.53 13.47L6.62 20.38A2.12 2.12 0 0 1 3.62 17.38L10.53 10.47A6 6 0 0 1 18.47 2.53L14.71 6.29Z" fill="currentColor"></path><path d="M14.7 6.3A1 1 0 0 0 14.7 7.7L16.3 9.3A1 1 0 0 0 17.7 9.3L21.47 5.53A6 6 0 0 1 13.53 13.47L6.62 20.38A2.12 2.12 0 0 1 3.62 17.38L10.53 10.47A6 6 0 0 1 18.47 2.53L14.71 6.29Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-download" viewBox="0 0 24 24" fill="none"><path d="M4 15V17C4 18.657 5.343 20 7 20H17C18.657 20 20 18.657 20 17V15" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M12 4V14M12 14L8 10M12 14L16 10" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-warning" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M10.24 3.99L2.4 17.5C1.62 18.83 2.58 20.5 4.12 20.5H19.88C21.42 20.5 22.38 18.83 21.6 17.5L13.76 3.99C12.98 2.66 11.02 2.66 10.24 3.99Z" fill="currentColor"></path><path d="M10.24 3.99L2.4 17.5C1.62 18.83 2.58 20.5 4.12 20.5H19.88C21.42 20.5 22.38 18.83 21.6 17.5L13.76 3.99C12.98 2.66 11.02 2.66 10.24 3.99Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M12 9V13M12 16.5H12.01" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-error" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M22 12C22 17.5228 17.5228 22 12 22C6.47715 22 2 17.5228 2 12C2 6.47715 6.47715 2 12 2C17.5228 2 22 6.47715 22 12Z" fill="currentColor"></path><path d="M22 12C22 17.5228 17.5228 22 12 22C6.47715 22 2 17.5228 2 12C2 6.47715 6.47715 2 12 2C17.5228 2 22 6.47715 22 12Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M15 9L9 15M9 9L15 15" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-info" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M22 12C22 17.5228 17.5228 22 12 22C6.47715 22 2 17.5228 2 12C2 6.47715 6.47715 2 12 2C17.5228 2 22 6.47715 22 12Z" fill="currentColor"></path><path d="M22 12C22 17.5228 17.5228 22 12 22C6.47715 22 2 17.5228 2 12C2 6.47715 6.47715 2 12 2C17.5228 2 22 6.47715 22 12Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M12 11V16.5M12 7.5H12.01" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-refresh" viewBox="0 0 24 24" fill="none"><path d="M21 12A9 9 0 1 1 18.36 5.64L21 8" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M21 3V8H16" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-sun" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M16 12C16 14.209 14.209 16 12 16C9.791 16 8 14.209 8 12C8 9.791 9.791 8 12 8C14.209 8 16 9.791 16 12Z" fill="currentColor"></path><path d="M16 12C16 14.209 14.209 16 12 16C9.791 16 8 14.209 8 12C8 9.791 9.791 8 12 8C14.209 8 16 9.791 16 12Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path><path d="M12 2V4M12 20V22M4.93 4.93L6.34 6.34M17.66 17.66L19.07 19.07M2 12H4M20 12H22M4.93 19.07L6.34 17.66M17.66 6.34L19.07 4.93" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+<symbol id="i-moon" viewBox="0 0 24 24" fill="none"><path opacity="0.12" d="M21 12.79A9 9 0 1 1 11.21 3A7 7 0 0 0 21 12.79Z" fill="currentColor"></path><path d="M21 12.79A9 9 0 1 1 11.21 3A7 7 0 0 0 21 12.79Z" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"></path></symbol>
+</svg>
@@ -0,0 +1,87 @@
+/* Strata UI — tokens. Derived from the Gainsty design system.
+   Light is default; dark via <html data-theme="dark">.
+   Values marked (derived) are not in the Gainsty source and were added for the app. */
+
+/* Outfit (SIL Open Font License 1.1, serve/web/fonts/OFL.txt): one variable font, weights 100-900 */
+@font-face{font-family:"Outfit";src:url("/fonts/outfit-latin-wght.woff2") format("woff2-variations");font-weight:100 900;font-display:swap;unicode-range:U+0000-00FF,U+0131,U+0152-0153,U+02BB-02BC,U+02C6,U+02DA,U+02DC,U+0304,U+0308,U+0329,U+2000-206F,U+20AC,U+2122,U+2191,U+2193,U+2212,U+2215,U+FEFF,U+FFFD}
+@font-face{font-family:"Outfit";src:url("/fonts/outfit-latin-ext-wght.woff2") format("woff2-variations");font-weight:100 900;font-display:swap;unicode-range:U+0100-02BA,U+02BD-02C5,U+02C7-02CC,U+02CE-02D7,U+02DD-02FF,U+0304,U+0308,U+0329,U+1D00-1DBF,U+1E00-1E9F,U+1EF2-1EFF,U+2020,U+20A0-20AB,U+20AD-20C0,U+2113,U+2C60-2C7F,U+A720-A7FF}
+
+:root{
+  /* type */
+  --st-font:"Outfit",system-ui,-apple-system,"Segoe UI",Roboto,sans-serif;
+  --st-font-mono:ui-monospace,"SF Mono","Cascadia Code",Consolas,"Liberation Mono",monospace; /* (derived) */
+  --st-fs-xs:12px;  --st-fs-sm:13px; --st-fs-md:14px; --st-fs-base:16px;
+  --st-fs-lg:18px;  --st-fs-xl:24px; --st-fs-stat:32px; --st-fs-display:40px;
+  --st-fw-regular:400; --st-fw-medium:500; --st-fw-bold:700; --st-fw-black:900;
+  --st-lh-tight:1.1; --st-lh-body:1.55;
+
+  /* spacing (4pt) */
+  --st-s-1:4px; --st-s-2:8px; --st-s-3:12px; --st-s-4:16px; --st-s-5:20px;
+  --st-s-6:24px; --st-s-8:32px; --st-s-10:40px; --st-s-12:48px; --st-s-16:64px;
+
+  /* radius */
+  --st-r-sm:8px; --st-r-md:14px; --st-r-lg:20px; --st-r-xl:24px; --st-r-pill:9999px;
+
+  /* motion (derived) */
+  --st-ease:cubic-bezier(.2,.7,.2,1); --st-dur:160ms;
+
+  /* color — light */
+  --st-bg:#f9fafb;
+  --st-surface:#ffffff;
+  --st-surface-2:#f7f7f7;
+  --st-line:#dee5ee;
+  --st-line-soft:#eeeeee;
+  --st-ink:#221e1f;
+  --st-ink-soft:#303030;
+  --st-ink-muted:#6f6f6f;        /* Gainsty #7a7a7a darkened slightly for 4.5:1 on white */
+  --st-icon:#2F384C;             /* Anron default */
+
+  --st-accent:#10b981;
+  --st-accent-hover:#0c8a60;
+  --st-accent-ink:#ffffff;
+  --st-accent-tint:#ecfdf5;
+  --st-accent-text:#0c8a60;      /* accent used as text on light */
+  --st-focus:#405de6;
+
+  --st-info:#0f97ff;     --st-info-tint:#e8f4ff;    --st-info-text:#0668c2;
+  --st-warn:#e39b0b;     --st-warn-tint:#fff6e0;    --st-warn-text:#8a5a00;   /* (derived) */
+  --st-danger:#e0283f;   --st-danger-tint:#fdecee;  --st-danger-text:#b01a2e; /* (derived) */
+
+  --st-code-bg:#1b1f23;  --st-code-ink:#e6e9ec;  /* code blocks stay dark in both themes (derived) */
+
+  --st-sh-card:0 18px 7px rgba(0,0,0,.01),0 10px 6px rgba(0,0,0,.02),0 4px 4px rgba(0,0,0,.03),0 1px 2px rgba(0,0,0,.04);
+  --st-sh-bar:0 54px 21px rgba(0,0,0,.01),0 30px 18px rgba(0,0,0,.03),0 13px 13px rgba(0,0,0,.05),0 3px 7px rgba(0,0,0,.06);
+  --st-sh-pop:0 10px 40px rgba(15,23,42,.08),0 1px 3px rgba(15,23,42,.06);
+  color-scheme:light;
+}
+
+/* color — dark (all derived) */
+[data-theme="dark"]{
+  --st-bg:#0e1113;
+  --st-surface:#161a1d;
+  --st-surface-2:#1d2226;
+  --st-line:#2a3036;
+  --st-line-soft:#22272c;
+  --st-ink:#eef1f3;
+  --st-ink-soft:#c7cdd3;
+  --st-ink-muted:#8e979f;
+  --st-icon:#c7cdd3;
+
+  --st-accent:#34d399;
+  --st-accent-hover:#4ade80;
+  --st-accent-ink:#052e20;
+  --st-accent-tint:rgba(52,211,153,.12);
+  --st-accent-text:#4ade80;
+  --st-focus:#7b93ff;
+
+  --st-info:#3aa9ff;     --st-info-tint:rgba(58,169,255,.12);  --st-info-text:#7cc4ff;
+  --st-warn:#f5b53d;     --st-warn-tint:rgba(245,181,61,.12);  --st-warn-text:#f8c96a;
+  --st-danger:#ff5a6e;   --st-danger-tint:rgba(255,90,110,.12);--st-danger-text:#ff8a98;
+
+  --st-code-bg:#0a0c0e;  --st-code-ink:#e6e9ec;
+
+  --st-sh-card:0 1px 2px rgba(0,0,0,.4);
+  --st-sh-bar:0 12px 32px rgba(0,0,0,.45),0 2px 6px rgba(0,0,0,.35);
+  --st-sh-pop:0 16px 48px rgba(0,0,0,.55),0 1px 3px rgba(0,0,0,.4);
+  color-scheme:dark;
+}
@@ -21,6 +21,8 @@ What the first run does (each step is skipped when it is already done):

 Options: --family qwen|swift, --model Q2_0|IQ2_XS|IQ3_XXS|IQ3_S, --context 32768, --vision yes|no|gpu|cpu, --port 8080, --yes (recommended
 answers, no questions), --setup (install another model / change settings instead of starting), --no-start,
+--host 0.0.0.0 --api-key KEY (reach it from other devices on your network), --experimental-speed-projection on|off
+(EXPERIMENTAL, off by default),
 --models-dir DIR, --gguf-dir DIR (use GGUF files you already have), --build (compile instead of the ready-made
 engine), --check (only check this PC).
 """
@@ -54,8 +56,8 @@ PREBUILT_ASSET = "strata-windows-x64.zip" if WIN else "strata-linux-x64.zip"
 # the CUDA libraries the ready-made engine loads (the same CUDA 13.0 it is built with), from NVIDIA's pip packages
 CUDA_WHEELS = ["nvidia-cublas==13.0.2.14", "nvidia-cuda-runtime==13.0.96"]
 MIN_DRIVER = 580                       # CUDA 13.0
-MIN_ENGINE = (0, 1, 7)                 # per-request sampling on the GEN line (the server sends it), v0.1.7
-PY_PACKAGES = ["numpy", "jinja2", "regex", "pyyaml", "tqdm", "requests", "cmake", "ninja", "pillow"]
+MIN_ENGINE = (0, 1, 10)                # INFO arena_mib: the start-up RAM warning (issue #27), v0.1.10
+PY_PACKAGES = ["numpy", "jinja2", "regex", "pyyaml", "tqdm", "requests", "cmake", "ninja", "pillow", "psutil"]

 MODELS = {
    "Q2_0": {"about": "2-bit, the fastest", "download_gb": 66.4, "ram_gb": 48, "arena_gb": 34.0},
@@ -86,6 +88,8 @@ FAMILIES = {
              "license": "Swift Open License 1.0: https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF"},
 }
 MMPROJ = "mmproj-Qwen3.8-Flash-Next-BF16.gguf"
+# EXPERIMENTAL, off by default (setup asks): a control vector shipped with the repository, see its README
+ESP_VECTOR = ROOT / "data" / "experimental-speed-projection" / "Qwen3.8-Flash-Next-experimental-speed-projection.gguf"
 # the image encoder on the GPU (~1.2 GB at 1024 image tokens) warms up before the engine starts, so the engine
 # sizes its expert slots around it and the default reserve (700 MiB) is enough; engines before 0.1.2 need more
 VISION = {"gpu": {"max_tokens": 1024, "reserve_mib": 700},
@@ -647,9 +651,18 @@ def main() -> int:
    ap.add_argument("--family", choices=list(FAMILIES), help="qwen = Qwen3.8-Flash-Next, swift = Swift 1.5")
    ap.add_argument("--model", choices=list(MODELS))
    ap.add_argument("--context", type=int)
+    ap.add_argument("--kv", choices=["int8", "q4_0"],
+                    help="KV cache precision above 8K context: int8 (default) or q4_0 (half the memory, a little less "
+                         "precise)")
    ap.add_argument("--vision", choices=["yes", "no", "none", "gpu", "cpu"],
                    help="let the model read images (yes = the encoder on the GPU)")
+    ap.add_argument("--experimental-speed-projection", metavar="on|off|GGUF",
+                    help="EXPERIMENTAL, off by default: the control vector in data/experimental-speed-projection "
+                         "(or another GGUF) as a projection on layers 4-44; see docs/DETAILS.md")
    ap.add_argument("--port", type=int, default=8080)
+    ap.add_argument("--host", help="where the server listens: 127.0.0.1 = this PC only (default), 0.0.0.0 = also other "
+                                   "devices on your network (issue #26; set --api-key too)")
+    ap.add_argument("--api-key", help="require this key from clients (recommended with --host 0.0.0.0)")
    ap.add_argument("--models-dir", default=str(ROOT / "models"), help="where the GGUF files go (~70 GB)")
    ap.add_argument("--gguf-dir", help="use GGUF files you already have (a folder with the two shards)")
    ap.add_argument("--yes", action="store_true", help="accept the recommended answers")
@@ -759,6 +772,17 @@ def main() -> int:
             "+ the context): using 128K")
        ctx = 131072
    ok(f"context: {ctx} tokens")
+    # the KV cache (the model's memory of the conversation): 8-bit, or 4-bit after a Hadamard rotation (PR #21)
+    kv = "fp16" if ctx <= 8192 else (a.kv or "int8")
+    if ctx > 8192 and not a.kv and not a.yes:
+        say()
+        say("  KV cache precision (the model's memory of the conversation):")
+        say("  1) 8-bit   (recommended: what every published number was measured with)")
+        say("  2) 4-bit   half the memory (about 4% faster at 128K), but measurably less precise on long")
+        say("             documents; long-context lookups (needle tests) still pass")
+        kv = ["int8", "q4_0"][int(ask("KV cache?", ["1", "2"], "1", a.yes)) - 1]
+    if ctx > 8192:
+        ok(f"KV cache: {'8-bit' if kv == 'int8' else '4-bit (Hadamard-rotated)'}")
    if a.vision:
        vision = {"yes": "gpu", "no": "none"}.get(a.vision, a.vision)
    else:
@@ -767,6 +791,25 @@ def main() -> int:
        say("  download and keeps ~1.4 GB of VRAM free for the image encoder, so text is a few % slower.")
        vision = "gpu" if ask("Do you want images?", ["y", "n"], "n", a.yes) == "y" else "none"
    ok("images: " + {"none": "off", "gpu": "on", "cpu": "on (encoder on the CPU)"}[vision])
+    # EXPERIMENTAL: the experimental-speed-projection control vector (data/experimental-speed-projection), off unless
+    # chosen here; with it loaded, the web app and the API switch it off per request
+    esp = None
+    esp_choice = (a.experimental_speed_projection or "").strip()
+    if family == "qwen":
+        if not esp_choice:
+            say()
+            say("  EXPERIMENTAL - speed projection: a small control vector applied while the model runs (layers 4-44).")
+            say("  It changes how the model answers: its package describes it as a refusal-direction projection (the")
+            say("  model declines far fewer requests). Off unless you choose it; when on, the web app can switch it off")
+            say("  per chat. Details: data/experimental-speed-projection/README.md")
+            esp_choice = "on" if ask("Turn on the experimental speed projection?", ["y", "n"], "n", a.yes) == "y" else "off"
+        if esp_choice.lower() not in ("off", "no", "n", "0"):
+            esp = ESP_VECTOR if esp_choice.lower() in ("on", "yes", "y", "1") else Path(esp_choice).expanduser().resolve()
+            if not esp.is_file():
+                fail(f"the experimental speed projection's vector is missing: {esp}")
+        ok("experimental speed projection: " + ("ON (experimental)" if esp else "off"))
+    elif esp_choice.lower() not in ("", "off", "no", "n", "0"):
+        warn("the experimental speed projection is made for the original Qwen3.8-Flash-Next, not Swift 1.5: left off")
    models_dir = Path(a.gguf_dir) if a.gguf_dir else Path(a.models_dir) / tag
    shards = [models_dir / fam["file"].format(q=model, i=i) for i in (1, 2)]
    have_model = all(s.exists() and (done(s) or a.gguf_dir) for s in shards)
@@ -777,7 +820,7 @@ def main() -> int:

    # ---- 3. python packages
    step(3, "Python packages")
-    pip_install(PY_PACKAGES, "numpy, jinja2, regex, pyyaml, tqdm, requests, cmake, ninja, pillow")
+    pip_install(PY_PACKAGES, "numpy, jinja2, regex, pyyaml, tqdm, requests, cmake, ninja, pillow, psutil")

    # ---- 4. the engine
    step(4, "the Strata engine")
@@ -869,19 +912,27 @@ def main() -> int:
            "--prefill", "2048", "--spec", "4", "--spec-min-p", "0.5", "--mtp", str(rt),
            "--max-context", str(ctx)]
    if ctx > 8192:
-        args += ["--kv", "int8"]
+        args += ["--kv", kv]
    # KV streaming: from 64K up the whole KV cache lives in RAM and only the part the attention reads (32K positions
    # per layer) stays in VRAM; the VRAM it frees holds more experts (+6% at 128K, +23% at 262K with Q2_0). It
-    # costs ~13.7 KB of RAM per context token (1.7 GB at 128K, 3.4 GB at 262K), so only when that fits.
-    kv_ram_gb = ctx * 13728 / 1e9
+    # costs ~13.7 KB of RAM per context token with 8-bit KV (1.7 GB at 128K), 7.5 KB with 4-bit, so only when it fits.
+    kv_ram_gb = ctx * (13 * (576 if kv == "q4_0" else 1056)) / 1e9   # 12 QSA layers + the draft layer
    if ctx >= 65536 and ram >= MODELS[model]["ram_gb"] + kv_ram_gb + 1:
        args += ["--kv-resident", "32768"]
        ok(f"KV streaming on: the context's KV cache lives in RAM ({kv_ram_gb:.1f} GB), more experts fit in VRAM")
    if vision != "none":
        args += ["--vision", "--vram-reserve-mib", str(VISION[vision]["reserve_mib"])]
+    if esp is not None:
+        # the package's profile, with llama.cpp's flags (the engine takes the same ones)
+        args += ["--control-vector-scaled", f"{esp}:1.0", "--control-vector-layer-range", "4", "44",
+                 "--cvec-mode", "project", "--cvec-dir", "per-layer"]
    cfg = {"exe": str(eng / EXE), "args": args, "cwd": str(ROOT), "tokenizer": str(pack / "tokenizer"),
           "model_name": f"{fam['name']}-{model.lower()}", "log": str(ROOT / f"strata-{tag.lower()}.log"),
           "lib_dirs": lib_dirs, "port": a.port}
+    if a.host:
+        cfg["host"] = a.host
+    if a.api_key:
+        cfg["api_key"] = a.api_key
    if vision != "none":
        cfg["vision"] = {"exe": str(eng / VEXE), "mmproj": str(mmproj), "model": str(shards[0]),
                         "gpu": vision == "gpu", "max_tokens": VISION[vision]["max_tokens"]}
@@ -896,6 +947,9 @@ def main() -> int:
    say("All set.")
    say(f"  API (OpenAI):     http://127.0.0.1:{a.port}/v1   (any API key; model name: anything)")
    say(f"  API (Anthropic):  http://127.0.0.1:{a.port}/v1/messages")
+    if a.host and a.host not in ("127.0.0.1", "localhost"):
+        say(f"  Other devices:    the server window prints this PC's address (http://<IP>:{a.port}/)"
+            + ("" if a.api_key else " - no API key set: anyone on your network can use it"))
    say(f"  Next time:        just run {'START-HERE.bat' if WIN else './setup.sh'} (or {script.name}) - it starts right away")
    if vision != "none":
        say("  Images:           send them in the chat page, in chat.py (/image <path>) or over the API")
@@ -7,6 +7,7 @@
 #include "strata/kernels/elementwise.hpp"
 #include "strata/kernels/gdn.hpp"
 #include "strata/kernels/qsa.hpp"
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/kernels/kv_q8.hpp"
 #include "strata/kernels/quantize_act.hpp"
 #include "strata/kernels/rope.hpp"
@@ -24,6 +25,7 @@
 #include "strata/kernels/native_rope.hpp"
 #include "strata/kernels/native_flash_attn.hpp"
 #include "strata/kernels/fused_gr.hpp"
+#include "strata/kernels/cvec.hpp"
 #include "strata/kernels/qsa_decode_attn.hpp"
 #include "strata/kernels/fused_gdn.hpp"
 #include "strata/kernels/qsa_select.hpp"
@@ -170,9 +172,11 @@ if (!w.wants_q8k()) {        s_gemv_q8_0_split(x80, p.codes, p.scales, p.offset,
 // namespace
 void layer_set_native_bf16(bool enabled) { native_bf16_projections = enabled; }
 void layer_set_native_flash_attn_short(bool enabled) { native_flash_attn_short = enabled; }
-namespace { bool g_kv_int8 = false; }
+namespace { bool g_kv_int8 = false, g_kv_q4 = false; }
 void qsa_set_kv_int8(bool enabled) { g_kv_int8 = enabled; }
 bool qsa_kv_int8() { return g_kv_int8; }
+void qsa_set_kv_q4(bool enabled) { g_kv_q4 = enabled; }
+bool qsa_kv_q4() { return g_kv_q4; }
 uint64_t gdn_buffers_bytes(const ModelGeometry& g) {    const int64_t C = g.ssm_conv_channels;    const int64_t V = g.ssm_value_dim;    const uint64_t parts[] = {        q8k_bytes(g.n_embd),
 // x_q8k
 (uint64_t) (g.n_embd / 32) * 34,
@@ -507,6 +511,7 @@ KvPlan kv_plan(const QsaShapes& s, int64_t max_cells, int64_t ring_cells) {
    return p;
 }
 uint64_t kv_pool_bytes(const QsaShapes& s, int64_t pages) {
+    if (g_kv_q4) return (uint64_t) pages * s.page_size * strata::kernels::kv_q4_bytes_per_cell(s) + 64;
    return g_kv_int8 ? (uint64_t) pages * s.page_size * strata::kernels::kv_q8_bytes_per_cell(s) + 64
                     : (uint64_t) pages * s.n_head_kv * s.page_size * s.head_dim * 2 * 2;
 }
@@ -540,11 +545,16 @@ uint64_t qsa_state_init(const ModelGeometry& g, int64_t max_cells, void* base, Q
    const KvPlan p = kv_plan(s, max_cells, ring_cells);
    const int64_t pages = p.pages;
    Cursor c{(uint8_t*) base};
-    st.kv_int8 = g_kv_int8;
+    st.kv_int8 = g_kv_int8 && !g_kv_q4;
+    st.kv_q4 = g_kv_q4;
    st.kv_mode = p.mode;
    st.n_slots = p.slots;
    const uint64_t rows = (uint64_t) p.slots * s.n_head_kv * s.page_size;   // VRAM rows: the slots
-    if (g_kv_int8) {
+    const uint64_t q4_row = strata::kernels::kv_q4_bytes_per_head((int) s.head_dim);
+    if (st.kv_q4) {
+        st.k_q4 = c.take<uint8_t>(rows * q4_row);
+        st.v_q4 = c.take<uint8_t>(rows * q4_row);
+    } else if (g_kv_int8) {
        st.k_q = c.take<int8_t>(rows * s.head_dim);
        st.v_q = c.take<int8_t>(rows * s.head_dim);
        st.k_scale = c.take<uint16_t>(rows * (s.head_dim / strata::kernels::KV_Q8_GROUP));
@@ -595,7 +605,7 @@ uint64_t qsa_state_init(const ModelGeometry& g, int64_t max_cells, void* base, Q
    st.host = strata::kernels::KvHostPools{};
    if (p.mode != 0) {
        const uint64_t hrows = (uint64_t) pages * s.n_head_kv * s.page_size;
-        const uint64_t bytes = (uint64_t) pages * strata::kernels::kv_block_bytes(s, g_kv_int8) + 4 * 256;
+        const uint64_t bytes = (uint64_t) pages * strata::kernels::kv_block_bytes(s, qsa_kv_format(st)) + 4 * 256;
        uint8_t* h = nullptr;
        uint8_t* d = nullptr;
        if (cudaHostAlloc((void**) &h, bytes, cudaHostAllocMapped | cudaHostAllocPortable) != cudaSuccess ||
@@ -606,7 +616,10 @@ uint64_t qsa_state_init(const ModelGeometry& g, int64_t max_cells, void* base, Q
        }
        g_kv_host_bytes += bytes;
        Cursor hc{d};
-        if (g_kv_int8) {
+        if (st.kv_q4) {
+            st.host.k_q4 = hc.take<uint8_t>(hrows * q4_row);
+            st.host.v_q4 = hc.take<uint8_t>(hrows * q4_row);
+        } else if (g_kv_int8) {
            st.host.k_q = hc.take<int8_t>(hrows * s.head_dim);
            st.host.v_q = hc.take<int8_t>(hrows * s.head_dim);
            st.host.k_scale = hc.take<uint16_t>(hrows * (s.head_dim / strata::kernels::KV_Q8_GROUP));
@@ -645,7 +658,10 @@ void qsa_state_zero(const QsaState& st, const ModelGeometry& g, void* stream) {
    const QsaShapes s = qsa_shapes(g);
    cudaStream_t cs = (cudaStream_t) stream;
    const size_t rows = (size_t) st.n_slots * s.n_head_kv * s.page_size;
-    if (st.kv_int8) {
+    if (st.kv_q4) {
+        cudaMemsetAsync(st.k_q4, 0, rows * strata::kernels::kv_q4_bytes_per_head((int) s.head_dim), cs);
+        cudaMemsetAsync(st.v_q4, 0, rows * strata::kernels::kv_q4_bytes_per_head((int) s.head_dim), cs);
+    } else if (st.kv_int8) {
        cudaMemsetAsync(st.k_q, 0, rows * s.head_dim, cs);
        cudaMemsetAsync(st.v_q, 0, rows * s.head_dim, cs);
        cudaMemsetAsync(st.k_scale, 0, rows * (s.head_dim / strata::kernels::KV_Q8_GROUP) * 2, cs);
@@ -666,7 +682,8 @@ void qsa_state_zero(const QsaState& st, const ModelGeometry& g, void* stream) {
 strata::kernels::QsaAttnPools qsa_attn_pools(const QsaState& st) {
    strata::kernels::QsaAttnPools pools;
    pools.page_table = st.page_table;
-    if (st.kv_int8) { pools.k_q = st.k_q; pools.v_q = st.v_q; pools.k_scale = st.k_scale; pools.v_scale = st.v_scale; }
+    if (st.kv_q4) { pools.k_q4 = st.k_q4; pools.v_q4 = st.v_q4; }
+    else if (st.kv_int8) { pools.k_q = st.k_q; pools.v_q = st.v_q; pools.k_scale = st.k_scale; pools.v_scale = st.v_scale; }
    else { pools.k_pool = st.k_pool; pools.v_pool = st.v_pool; }
    return pools;
 }
@@ -674,7 +691,7 @@ strata::kernels::QsaAttnPools qsa_attn_pools(const QsaState& st) {
 void qsa_kv_resolve(const QsaState& st, const ModelGeometry& g, const int32_t* ids, const int32_t* steps, int64_t n_q,
                    int64_t cap, void* stream) {
    if (st.kv_mode != 1) return;
-    strata::kernels::kv_stream_resolve(st.map, qsa_attn_pools(st), st.host, st.kv_int8, ids, steps, n_q, cap,
+    strata::kernels::kv_stream_resolve(st.map, qsa_attn_pools(st), st.host, qsa_kv_format(st), ids, steps, n_q, cap,
                                       qsa_shapes(g), stream);
 }

@@ -846,7 +863,11 @@ if (!gemv_quantized(*w_attnk, p_k, f_k, b.x_q8_0, b.x_q8k, b.kcur, g.n_embd, g.n
 // EVERY ENTRY POINT FROM HERE ON IS THE CAPTURABLE ONE: the per-token counts come from `st.step` and every
 // launch is sized from a capacity in `st`/`b`, so this sequence can be captured and replayed.  The
 // host-scalar wrappers would be correct here today and silently wrong in a graph.
-if (st.kv_int8) kv_append_q8_step(st.k_q, st.v_q, st.k_scale, st.v_scale, st.page_table, st.step, b.kcur, b.vcur, s, stream, &st.host);    else kv_append_step(st.k_pool, st.v_pool, st.page_table, st.step, b.kcur, b.vcur, s, stream, &st.host);    {        const uint64_t nvk = (uint64_t) g.n_head_kv * g.head_dim;        const uint64_t base = (uint64_t) 2 * g.n_embd + 2 * g.hc + (uint64_t) g.n_head * g.head_dim + 2 * nvk + 8;        if (!st.kv_int8) dump_slot(dump, g, layer, (const float*) st.k_pool, base, nvk / 2, stream);        if (!st.kv_int8) dump_slot(dump, g, layer, (const float*) st.v_pool, base + nvk / 2, nvk / 2, stream);        dump_slot(dump, g, layer, b.vcur, base + nvk, nvk, stream);        dump_slot(dump, g, layer, b.kcur, base + 2 * nvk, nvk, stream);    }    {        const QsaIndexerBuffers ib{st.idx_tail, st.idx_dead, st.idx_pooled, st.idx_block_pos};        if (native_qsa_indexer_enabled()) {
+if (st.kv_q4) {
+    strata::kernels::fwht256_inplace_cuda(b.kcur, g.n_head_kv, stream);   // Q4_0: rotated K and V (kv_q4.hpp)
+    strata::kernels::fwht256_inplace_cuda(b.vcur, g.n_head_kv, stream);
+    strata::kernels::kv_append_q4_step(st.k_q4, st.v_q4, st.page_table, st.step, b.kcur, b.vcur, s, stream, &st.host);
+} else if (st.kv_int8) kv_append_q8_step(st.k_q, st.v_q, st.k_scale, st.v_scale, st.page_table, st.step, b.kcur, b.vcur, s, stream, &st.host);    else kv_append_step(st.k_pool, st.v_pool, st.page_table, st.step, b.kcur, b.vcur, s, stream, &st.host);    {        const uint64_t nvk = (uint64_t) g.n_head_kv * g.head_dim;        const uint64_t base = (uint64_t) 2 * g.n_embd + 2 * g.hc + (uint64_t) g.n_head * g.head_dim + 2 * nvk + 8;        if (!st.kv_int8) dump_slot(dump, g, layer, (const float*) st.k_pool, base, nvk / 2, stream);        if (!st.kv_int8) dump_slot(dump, g, layer, (const float*) st.v_pool, base + nvk / 2, nvk / 2, stream);        dump_slot(dump, g, layer, b.vcur, base + nvk, nvk, stream);        dump_slot(dump, g, layer, b.kcur, base + 2 * nvk, nvk, stream);    }    {        const QsaIndexerBuffers ib{st.idx_tail, st.idx_dead, st.idx_pooled, st.idx_block_pos};        if (native_qsa_indexer_enabled()) {
    try {
        native_qsa_indexer_append(b.idx_raw, st.step + kStepPos, pos_base,
            (const float*) w_ikn->data, RMS_EPS, ib, s, st.max_cells, (float) qsa_freq_base(), stream);
@@ -880,14 +901,13 @@ int64_t max_blocks = (st.max_cells / s.idx_block) + 2;
    }
    // KV streaming: every block the selection names is made resident before anything reads it
    qsa_kv_resolve(st, g, b.ids, st.step, 1, cap, stream);
+    if (st.kv_q4) strata::kernels::fwht256_inplace_cuda(b.qcur, g.n_head, stream);   // <Hq, Hk> = <q, k>
    if (g_fast_attn && !native_flash_attn_short && dump == nullptr) {
-        strata::kernels::QsaAttnPools pools;
-        pools.k_pool = st.k_pool; pools.v_pool = st.v_pool; pools.k_q = st.k_q; pools.v_q = st.v_q;
-        pools.k_scale = st.k_scale; pools.v_scale = st.v_scale; pools.page_table = st.page_table;
-        if (!st.kv_int8) { pools.k_q = nullptr; pools.v_q = nullptr; }
+        const strata::kernels::QsaAttnPools pools = qsa_attn_pools(st);
        strata::kernels::qsa_decode_attn_step(b.qcur, pools, b.ids, st.step, cap, s, b.attn_scratch, b.attn, stream);
    } else {
-    if (st.kv_int8) kv_gather_q8_step(st.k_q, st.v_q, st.k_scale, st.v_scale, st.page_table, b.ids, st.step, cap, s,                                 b.k_scratch, b.v_scratch, stream);    else kv_gather_step(st.k_pool, st.v_pool, st.page_table, b.ids, st.step, cap, s, b.k_scratch, b.v_scratch,                   stream);    if (native_flash_attn_short) {
+    if (st.kv_q4) strata::kernels::kv_gather_q4_step(st.k_q4, st.v_q4, st.page_table, b.ids, st.step, cap, s, b.k_scratch, b.v_scratch, stream);
+    else if (st.kv_int8) kv_gather_q8_step(st.k_q, st.v_q, st.k_scale, st.v_scale, st.page_table, b.ids, st.step, cap, s,                                 b.k_scratch, b.v_scratch, stream);    else kv_gather_step(st.k_pool, st.v_pool, st.page_table, b.ids, st.step, cap, s, b.k_scratch, b.v_scratch,                   stream);    if (native_flash_attn_short) {
    if (st.max_cells < 1 || st.max_cells > 256 || !st.attention_status || !st.host_step) {
        err = v.name("native_flash_attn") + ": short adapter requires context <=256 and persistent status storage";
        return false;
@@ -902,6 +922,7 @@ int64_t max_blocks = (st.max_cells / s.idx_block) + 2;
    }
 } else qsa_attend_step(b.qcur, b.k_scratch, b.v_scratch, st.step, cap, s, b.attn, nullptr, stream);
    }
+    if (st.kv_q4) strata::kernels::fwht256_inplace_cuda(b.attn, g.n_head, stream);   // the output back: H is self-inverse
    dump_slot(dump, g, layer, b.attn, (uint64_t) 2 * g.n_embd + 2 * g.hc,                            (uint64_t) g.n_head * g.head_dim, stream);    {        const uint64_t vs = (uint64_t) 2 * g.n_embd + 2 * g.hc + (uint64_t) g.n_head * g.head_dim +                            (uint64_t) 2 * g.n_head_kv * g.head_dim;        dump_slot(dump, g, layer, (const float*) b.v_scratch, vs,                            (uint64_t) g.n_head_kv * g.head_dim / 2, stream);        dump_slot(dump, g, layer, (const float*) b.ids, vs + (uint64_t) g.n_head_kv * g.head_dim / 2, 4, stream);        dump_slot(dump, g, layer, (const float*) st.step,                            vs + (uint64_t) g.n_head_kv * g.head_dim / 2 + 4, 4, stream);    }
 // ---- 9. Gate the attention output before its projection. Native CUDA keeps F32
 // sigmoid/multiply arithmetic and uses Q8_1 for the native quantized projection.
@@ -1087,7 +1108,8 @@ bool block_layer_pre(const WeightTable& tables, const ModelGeometry& g, int64_t
    // the device half: `ple_block` reading `emb_dev`, and the history shift.
    const bool fused = g_fused_gr && stage_prefix == 0 && half == 0 &&
                       strata::kernels::fused_gr_supported(g.n_embd, g.hc, g.hc_lr);
-    bool pending_ffn = fused && layer > 0;   // layer-1's FFN write has not been applied to R yet
+    // layer-1's FFN write has not been applied to R yet - unless a control vector follows it, which needs R
+    bool pending_ffn = fused && layer > 0 && !strata::kernels::cvec().covers(layer - 1);
    if (ple != nullptr && ple->ready() && layer == 1 && (half == 0 || half == 1)) {
        if (pending_ffn) {
            const strata::kernels::GrShapes gs0{g.n_embd, g.hc, g.hc_lr};
@@ -1222,10 +1244,23 @@ st_begin(layer, 5, stream);
    if (g_shared_early ? !moe_combine_parts(g, layer, k, mb, parts, bb.block_out, stream, err)
                       : !moe_finish(tables, g, layer, k, mb, bb.mixed, parts, bb.block_out, stream, err)) return false;
    st_end(layer, 5, stream);    dump_half(bb, g, layer, bb.block_out, (uint64_t) g.n_embd, g.n_embd, stream);    dump_half(bb, g, layer, bb.inject, (uint64_t) 2 * g.n_embd + g.hc, g.hc, stream);    st_begin(layer, 6, stream);
-    if (!(g_fused_gr && strata::kernels::fused_gr_supported(g.n_embd, g.hc, g.hc_lr)))
-        gr_write(bb.R, bb.block_out, bb.inject, gs, bb.R, stream);
-    else if (layer == g.n_layers - 1)
-        gr_write(bb.R, bb.block_out, bb.inject2, gs, bb.R, stream);   // materialise R for the head
+    const bool steer = strata::kernels::cvec().covers(layer);   // --control-vector-scaled: after this write
+    try {
+        if (!(g_fused_gr && strata::kernels::fused_gr_supported(g.n_embd, g.hc, g.hc_lr))) {
+            gr_write(bb.R, bb.block_out, bb.inject, gs, bb.R, stream);
+            if (steer) strata::kernels::cvec_apply(bb.R, layer, 1, g.hc * g.n_embd, nullptr, 0, nullptr, 0, false, stream);
+        } else if (layer == g.n_layers - 1) {
+            gr_write(bb.R, bb.block_out, bb.inject2, gs, bb.R, stream);   // materialise R for the head
+            if (steer) strata::kernels::cvec_apply(bb.R, layer, 1, g.hc * g.n_embd, nullptr, 0, nullptr, 0, false, stream);
+        } else if (steer) {
+            // the write the next layer's fused read would have folded, then the vector
+            strata::kernels::cvec_apply(bb.R, layer, 1, g.hc * g.n_embd, bb.block_out, g.n_embd, bb.inject2, g.hc,
+                                        true, stream);
+        }
+    } catch (const std::exception& e) {
+        err = std::string("block_layer_post: ") + e.what();
+        return false;
+    }
    st_end(layer, 6, stream);    return true;}
 bool block_layer(const WeightTable& tables, const ModelGeometry& g, int64_t layer, int64_t pos, int32_t pos_base,                 const GdnBuffers& gb, const QsaState& qst, const QsaBuffers& qb, const MoEBuffers& mb,                 int64_t k, const BlockBuffers& bb, const float* parts, void* stream, std::string& err,                 const Doorbell* db, const PleRun* ple) {
 // The two halves back to back.  `parts` must be THIS layer's experts and must be ready before the call -
@@ -7,6 +7,7 @@
 #include "strata/kernels/elementwise.hpp"
 #include "strata/kernels/fused_gr.hpp"
 #include "strata/kernels/gr.hpp"
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/kernels/kv_q8.hpp"
 #include "strata/kernels/native_moe.hpp"
 #include "strata/kernels/native_mmvq.hpp"
@@ -338,7 +339,12 @@ bool MtpDrafter::record_forward(int T, int step_row0, cudaStream_t cs, std::stri
        native_mmvq(GGML_Q8_0, q8("self_attn.v_proj.weight"), xq_, vcur_, (int) N, (int) (NKV * HD), T, cs);
        for (int t = 0; t < T; ++t) {
            norm_rope(kcur_ + t * NKV * HD, f32("self_attn.k_norm.weight"), (int) NKV, (int) HD, pos + t * NH);
-            if (st_.kv_int8)
+            if (st_.kv_q4) {   // Q4_0 KV (kv_q4.hpp): rotated K and V
+                fwht256_inplace_cuda(kcur_ + t * NKV * HD, NKV, cs);
+                fwht256_inplace_cuda(vcur_ + t * NKV * HD, NKV, cs);
+                kv_append_q4_step(st_.k_q4, st_.v_q4, st_.page_table, step + t * 4, kcur_ + t * NKV * HD,
+                                  vcur_ + t * NKV * HD, s, cs, &st_.host);
+            } else if (st_.kv_int8)
                kv_append_q8_step(st_.k_q, st_.v_q, st_.k_scale, st_.v_scale, st_.page_table, step + t * 4,
                                  kcur_ + t * NKV * HD, vcur_ + t * NKV * HD, s, cs, &st_.host);
            else
@@ -355,10 +361,12 @@ bool MtpDrafter::record_forward(int T, int step_row0, cudaStream_t cs, std::stri
                return false;
            }
            norm_rope(qc, f32("self_attn.q_norm.weight"), (int) NH, (int) HD, pos + t * NH);
+            if (st_.kv_q4) fwht256_inplace_cuda(qc, NH, cs);
        }
        const QsaAttnPools pools = qsa_attn_pools(st_);
        if (window_ > 0) window_ids(const_cast<int32_t*>(step), T, (int) window_, ident_, cap_, cs);
        qsa_decode_attn_batch(qcur_, pools, ident_, step, cap_, s, attn_scratch_, attn_, T, cs);
+        if (st_.kv_q4) fwht256_inplace_cuda(attn_, (int64_t) T * NH, cs);
        for (int t = 0; t < T; ++t)
            native_qsa_gate_apply(attn_ + t * NH * HD, qfull_ + t * NH * 2 * HD, attn32_ + t * NH * HD, (int) NH, (int) HD, cs);
        native_quantize_q8_1(attn32_, xq_, (int) (NH * HD), T, cs);
@@ -507,7 +515,7 @@ void MtpDrafter::kv_restore(int64_t upto) {
    // the ring's blocks below `upto`, from the host copy: a checkpoint resume may have left later cells in them
    const strata::kernels::QsaShapes s = shapes_of(*g_);
    const int64_t b1 = (upto + s.page_size - 1) / s.page_size, b0 = std::max<int64_t>(0, b1 - st_.n_slots);
-    strata::kernels::kv_ring_restore(qsa_attn_pools(st_), st_.host, st_.kv_int8, b0, b1, st_.n_slots, s, cs_);
+    strata::kernels::kv_ring_restore(qsa_attn_pools(st_), st_.host, qsa_kv_format(st_), b0, b1, st_.n_slots, s, cs_);
    cudaStreamSynchronize(cs_);
 }

@@ -11,7 +11,9 @@
 #include "strata/kernels/cpu/expert.hpp"
 #include "strata/kernels/elementwise.hpp"
 #include "strata/kernels/fused_gr.hpp"
+#include "strata/kernels/cvec.hpp"
 #include "strata/kernels/gr.hpp"
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/kernels/kv_q8.hpp"
 #include "strata/kernels/native_mmvq.hpp"
 #include "strata/kernels/native_qsa.hpp"
@@ -330,7 +332,9 @@ bool Verifier::record_window(int T, cudaStream_t cs, std::string& err) {
            wi[h] = need(v, (std::string(pfx[h]) + "inject.weight").c_str(), err);
            if (!wn[h] || !wd[h] || !wu[h] || !wi[h]) return false;
        }
-        bool pending = l > 0;   // the previous layer's FFN write, folded into this layer's first read
+        // the previous layer's FFN write, folded into this layer's first read (a control vector after it has
+        // already applied it)
+        bool pending = l > 0 && !cvec().covers(l - 1);
        if (l == 1 && ple_on) {
            float* normalized = (float*) ((uint8_t*) ss.ple.scratch + ple_block_scratch_bytes());
            for (int t = tb; t < te; ++t) {
@@ -421,10 +425,17 @@ bool Verifier::record_window(int T, cudaStream_t cs, std::string& err) {
                native_mmvq(wk->native_type, wk->native_data, xq_, kcur_ + tb * NKV * HD, (int) N, (int) (NKV * HD), n, cs);
                native_mmvq(wv->native_type, wv->native_data, xq_, vcur_ + tb * NKV * HD, (int) N, (int) (NKV * HD), n, cs);
                for (int t = tb; t < te; ++t) norm_rope(kcur_ + t * NKV * HD, wkn, (int) NKV, (int) HD, pos_ + t * NH);
+                if (st.kv_q4) {   // Q4_0 KV (kv_q4.hpp): K and V rotated before they are stored
+                    fwht256_inplace_cuda(kcur_ + tb * NKV * HD, (int64_t) n * NKV, cs);
+                    fwht256_inplace_cuda(vcur_ + tb * NKV * HD, (int64_t) n * NKV, cs);
+                }
                if (grp == 0) copy_from_mapped(tail_snap_ + (size_t) qi * TS, st.idx_tail, TS, cs);
                for (int t = tb; t < te; ++t) {
                    const int32_t* step_t = step_ + t * kStepCount;
-                    if (st.kv_int8)
+                    if (st.kv_q4)
+                        kv_append_q4_step(st.k_q4, st.v_q4, st.page_table, step_t, kcur_ + t * NKV * HD,
+                                          vcur_ + t * NKV * HD, s, cs, &st.host);
+                    else if (st.kv_int8)
                        kv_append_q8_step(st.k_q, st.v_q, st.k_scale, st.v_scale, st.page_table, step_t,
                                          kcur_ + t * NKV * HD, vcur_ + t * NKV * HD, s, cs, &st.host);
                    else
@@ -446,6 +457,7 @@ bool Verifier::record_window(int T, cudaStream_t cs, std::string& err) {
                        return false;
                    }
                    norm_rope(qc, wqn, (int) NH, (int) HD, pos_ + t * NH);
+                    if (st.kv_q4) fwht256_inplace_cuda(qc, NH, cs);   // <Hq, Hk> = <q, k>
                }
                for (int t = tb; t < te; ++t) {
                    float* qx = qidx_ + t * IQ * ID;
@@ -461,6 +473,7 @@ bool Verifier::record_window(int T, cudaStream_t cs, std::string& err) {
                const QsaAttnPools pools = qsa_attn_pools(st);
                qsa_decode_attn_batch(qcur_ + tb * NH * HD, pools, sel_ + (size_t) tb * cap_, step_ + tb * kStepCount, cap_,
                                      s, attn_scratch_ + (size_t) tb * attn_scratch_floats_, attn_ + tb * NH * HD, n, cs);
+                if (st.kv_q4) fwht256_inplace_cuda(attn_ + tb * NH * HD, (int64_t) n * NH, cs);   // back: H^-1 = H
                for (int t = tb; t < te; ++t) {
                    if (native_qsa_enabled())
                        native_qsa_gate_apply(attn_ + t * NH * HD, qfull_ + t * NH * 2 * HD, attn32_ + t * NH * HD,
@@ -559,8 +572,12 @@ bool Verifier::record_window(int T, cudaStream_t cs, std::string& err) {
            mb.weights = w_ + t * K; mb.shared = shared_ + t * N;
            if (!moe_combine_parts(g, l, K, mb, parts_ + (size_t) t * K * N, bo_ + t * N, cs, err)) return false;
        }
-        if (l == g.n_layers - 1)
+        if (l == g.n_layers - 1) {
            for (int t = tb; t < te; ++t) gr_write(Rt(t), bo_ + t * N, inj2_ + t * HC, gs, Rt(t), cs);
+            if (cvec().covers(l)) cvec_apply(Rt(tb), l, n, HC * N, nullptr, 0, nullptr, 0, false, cs);
+        } else if (cvec().covers(l)) {
+            cvec_apply(Rt(tb), l, n, HC * N, bo_ + tb * N, N, inj2_ + tb * HC, HC, true, cs);
+        }
        return true;
    };

@@ -0,0 +1,128 @@
+// src/kernels/cuda/cvec.cu - see include/strata/kernels/cvec.hpp.
+#include "strata/kernels/cvec.hpp"
+
+#include <cuda_runtime.h>
+
+#include <stdexcept>
+
+namespace strata::kernels {
+namespace {
+
+constexpr int THREADS = 256;
+constexpr int MAXK = 16;   // n_embd up to 4096, held in registers between the dot and the update
+
+Cvec g_cvec;
+int* g_on = nullptr;
+bool g_on_host = false;
+
+// the fused hyper-connection read's gate (fused_gr.cu), so a write done here is bitwise the one it would have folded
+__device__ __forceinline__ float sigmoidf_(float x) { return 1.0f / (1.0f + __expf(-x)); }
+
+// one block per (stream, token): the pending write, then h . v over the stream, then the update
+__global__ void cvec_kernel(float* __restrict__ R, const float* __restrict__ dir, const float* __restrict__ s_l,
+                            const int* __restrict__ on, int mode, int64_t layer, int n, int hc, int64_t r_ld,
+                            const float* __restrict__ bo, int64_t bo_ld, const float* __restrict__ inj,
+                            int64_t inj_ld, int write) {
+    const int c = blockIdx.x;
+    const int64_t t = blockIdx.y;
+    float* r = R + t * r_ld + (int64_t) c * n;
+    const float s = s_l[layer];
+    const bool steer = *on != 0 && s != 0.0f;   // uniform over the block
+    if (!steer && !write) return;
+    const float* v = dir + layer * n;
+    const float w = write ? 2.0f * sigmoidf_(inj[t * inj_ld + c] / (float) hc) : 0.0f;
+    const float* b = write ? bo + t * bo_ld : nullptr;
+    float x[MAXK];
+    float dot = 0.0f;
+#pragma unroll
+    for (int k = 0; k < MAXK; ++k) {
+        const int d = threadIdx.x + k * THREADS;
+        if (d < n) {
+            float xv = r[d];
+            if (write) xv = fmaf(b[d], w, xv);
+            x[k] = xv;
+            if (steer && mode == 0) dot = fmaf(xv, v[d], dot);
+        }
+    }
+    if (steer && mode == 0) {
+        __shared__ float part[THREADS / 32];
+#pragma unroll
+        for (int o = 16; o > 0; o >>= 1) dot += __shfl_xor_sync(0xffffffffu, dot, o);
+        if ((threadIdx.x & 31) == 0) part[threadIdx.x >> 5] = dot;
+        __syncthreads();
+        if (threadIdx.x < 32) {
+            float p = threadIdx.x < THREADS / 32 ? part[threadIdx.x] : 0.0f;
+#pragma unroll
+            for (int o = 16; o > 0; o >>= 1) p += __shfl_xor_sync(0xffffffffu, p, o);
+            if (threadIdx.x == 0) part[0] = p;
+        }
+        __syncthreads();
+        dot = part[0] * s;   // s (h . v)
+    }
+#pragma unroll
+    for (int k = 0; k < MAXK; ++k) {
+        const int d = threadIdx.x + k * THREADS;
+        if (d < n) {
+            float xv = x[k];
+            if (steer) xv = mode == 0 ? fmaf(-dot, v[d], xv) : xv + v[d];
+            r[d] = xv;
+        }
+    }
+}
+
+}  // namespace
+
+const Cvec& cvec() { return g_cvec; }
+
+bool cvec_upload(const std::vector<float>& dir, const std::vector<float>& s, int mode, int first, int last,
+                 int64_t n_embd, int64_t hc, std::string& err) {
+    if (n_embd < 1 || n_embd > (int64_t) THREADS * MAXK) { err = "control vector: unsupported n_embd"; return false; }
+    if (s.empty() || dir.size() != s.size() * (size_t) n_embd) { err = "control vector: bad table sizes"; return false; }
+    float* d_dir = nullptr;
+    float* d_s = nullptr;
+    int* d_on = nullptr;
+    const int one = 1;
+    if (cudaMalloc(&d_dir, dir.size() * sizeof(float)) != cudaSuccess ||
+        cudaMalloc(&d_s, s.size() * sizeof(float)) != cudaSuccess || cudaMalloc(&d_on, sizeof(int)) != cudaSuccess ||
+        cudaMemcpy(d_dir, dir.data(), dir.size() * sizeof(float), cudaMemcpyHostToDevice) != cudaSuccess ||
+        cudaMemcpy(d_s, s.data(), s.size() * sizeof(float), cudaMemcpyHostToDevice) != cudaSuccess ||
+        cudaMemcpy(d_on, &one, sizeof(int), cudaMemcpyHostToDevice) != cudaSuccess) {
+        err = "control vector: device allocation failed";
+        return false;
+    }
+    g_cvec.dir = d_dir;
+    g_cvec.s = d_s;
+    g_cvec.on = d_on;
+    g_cvec.mode = mode;
+    g_cvec.first = first;
+    g_cvec.last = last;
+    g_cvec.n_embd = n_embd;
+    g_cvec.hc = hc;
+    g_cvec.steered.assign(s.size(), false);
+    for (size_t l = 0; l < s.size(); ++l) g_cvec.steered[l] = s[l] != 0.0f;
+    g_on = d_on;
+    g_on_host = true;
+    return true;
+}
+
+void cvec_set_enabled(bool on) {
+    if (g_on == nullptr || on == g_on_host) return;
+    cudaDeviceSynchronize();   // nothing in flight may still read the flag
+    const int v = on ? 1 : 0;
+    cudaMemcpy(g_on, &v, sizeof(int), cudaMemcpyHostToDevice);
+    g_on_host = on;
+}
+
+bool cvec_enabled() { return g_on != nullptr && g_on_host; }
+
+void cvec_apply(float* R, int64_t layer, int64_t T, int64_t r_ld, const float* bo, int64_t bo_ld, const float* inj,
+                int64_t inj_ld, bool write, void* stream) {
+    if (!g_cvec.loaded() || T < 1) return;
+    const dim3 grid((unsigned) g_cvec.hc, (unsigned) T);
+    cvec_kernel<<<grid, THREADS, 0, (cudaStream_t) stream>>>(R, g_cvec.dir, g_cvec.s, g_cvec.on, g_cvec.mode, layer,
+                                                            (int) g_cvec.n_embd, (int) g_cvec.hc, r_ld, bo, bo_ld,
+                                                            inj, inj_ld, write ? 1 : 0);
+    if (cudaPeekAtLastError() != cudaSuccess) throw std::runtime_error("cvec_apply: launch failed");
+}
+
+}  // namespace strata::kernels
@@ -0,0 +1,231 @@
+// src/kernels/cuda/kv_q4.cu - see include/strata/kernels/kv_q4.hpp. Q4_0 KV with Walsh-Hadamard rotation
+// (from PR #21 by code-martin; KV-streaming integration and the deterministic group maximum added on merge).
+#include "strata/kernels/kv_q4.hpp"
+#include "strata/kernels/f16_bits.hpp"
+#include "strata/kernels/kv_stream.hpp"
+
+#include <cuda_fp16.h>
+#include <cuda_runtime.h>
+#include <cstdio>
+#include <cstdlib>
+
+namespace strata::kernels {
+namespace {
+
+void check(const char* what) {
+    const cudaError_t e = cudaGetLastError();
+    if (e != cudaSuccess) {
+        std::fprintf(stderr, "kv_q4: %s: %s\n", what, cudaGetErrorString(e));
+        std::exit(1);
+    }
+}
+
+// Fast Walsh-Hadamard Transform for N = 256: one warp per row, 8 values per lane in registers.
+// Orthonormal (scale 1/sqrt(256) = 1/16), so it is its own inverse.
+__global__ void fwht256_kernel(const float* __restrict__ src, float* __restrict__ dst, int64_t n_rows, float scale) {
+    constexpr int warp_size = 32;
+    constexpr int N = 256;
+    constexpr int el_w = N / warp_size;   // 8
+
+    const int64_t r = (int64_t) blockIdx.x * blockDim.y + threadIdx.y;
+    if (r >= n_rows) return;
+
+    const float* row_src = src + r * N;
+    float* row_dst = dst + r * N;
+
+    float reg[el_w];
+    const int lane = threadIdx.x;
+
+#pragma unroll
+    for (int i = 0; i < el_w; ++i) reg[i] = row_src[i * warp_size + lane] * scale;
+
+    // the low 5 index bits live across lanes
+#pragma unroll
+    for (int h = 1; h < warp_size; h *= 2) {
+#pragma unroll
+        for (int j = 0; j < el_w; ++j) {
+            const float val = reg[j];
+            const float val2 = __shfl_xor_sync(0xffffffffu, val, h, warp_size);
+            reg[j] = (lane & h) == 0 ? val + val2 : val2 - val;
+        }
+    }
+    // the high 3 bits across each lane's registers
+#pragma unroll
+    for (int h = warp_size; h < N; h *= 2) {
+        const int step = h / warp_size;
+#pragma unroll
+        for (int j = 0; j < el_w; j += 2 * step) {
+#pragma unroll
+            for (int k = 0; k < step; ++k) {
+                const float x = reg[j + k];
+                const float y = reg[j + k + step];
+                reg[j + k] = x + y;
+                reg[j + k + step] = x - y;
+            }
+        }
+    }
+#pragma unroll
+    for (int i = 0; i < el_w; ++i) row_dst[i * warp_size + lane] = reg[i];
+}
+
+// One 32-value group in one warp (lane = element), ggml's q4_0: d = (the value of largest |x|) / -8,
+// q = clamp(trunc(x / d + 8.5), 0, 15). Returns the scale bits; `byte` is lane t's packed byte for t < 16
+// (element t in the low nibble, t + 16 in the high one). Ties in |x| resolve to the larger value in EVERY lane,
+// so all lanes agree on d (a plain `a > amax` could leave lanes with opposite signs and one block two scales).
+__device__ __forceinline__ uint16_t q4_group(float x, int lane, uint8_t& byte) {
+    float amax = fabsf(x), mval = x;
+#pragma unroll
+    for (int o = 16; o > 0; o >>= 1) {
+        const float a = __shfl_xor_sync(0xffffffffu, amax, o);
+        const float v = __shfl_xor_sync(0xffffffffu, mval, o);
+        if (a > amax || (a == amax && v > mval)) { amax = a; mval = v; }
+    }
+    const float d = mval / -8.0f;
+    const float id = d != 0.0f ? 1.0f / d : 0.0f;
+    int q = __float2int_rz(x * id + 8.5f);
+    const uint8_t qc = (uint8_t) (q < 0 ? 0 : (q > 15 ? 15 : q));
+    const uint8_t qhi = __shfl_down_sync(0xffffffffu, qc, 16);
+    byte = (uint8_t) (qc | (qhi << 4));
+    (void) lane;
+    return f16_from_f32(d);
+}
+
+__device__ __forceinline__ void q4_store(uint8_t* pool, long long row, int b, int lane, uint16_t d, uint8_t byte) {
+    block_q4_0* blk = reinterpret_cast<block_q4_0*>(pool + row * (long long) sizeof(block_q4_0) * 8) + b;
+    if (lane == 0) blk->d = d;
+    if (lane < 16) blk->qs[lane] = byte;
+}
+
+// One block = one 32-value group of one KV head of K (blockIdx.z = 0) or V (1); 32 threads. KV streaming: the VRAM
+// page only if the block is resident (table >= 0), the host copy always (identity layout) when there is one.
+__global__ void kv_append_q4_kernel(uint8_t* __restrict__ k_q4, uint8_t* __restrict__ v_q4,
+                                    const int32_t* __restrict__ table, const int32_t* __restrict__ step,
+                                    const float* __restrict__ kcur, const float* __restrict__ vcur,
+                                    int kv_heads, int head_dim, int page_size, KvHostPools host) {
+    const long long pos = (long long) __ldg(step + kStepPos);
+    const int h = blockIdx.x, b = blockIdx.y, t = threadIdx.x;
+    const bool is_v = blockIdx.z == 1;
+    const float x = (is_v ? vcur : kcur)[h * head_dim + b * QK4_0 + t];
+    uint8_t byte;
+    const uint16_t d = q4_group(x, t, byte);
+    const long long page = (long long) table[pos / page_size];
+    if (page >= 0) q4_store(is_v ? v_q4 : k_q4, (page * kv_heads + h) * page_size + (pos % page_size), b, t, d, byte);
+    if (host.k_q4 != nullptr)
+        q4_store(is_v ? host.v_q4 : host.k_q4, ((pos / page_size) * kv_heads + h) * page_size + (pos % page_size), b, t,
+                 d, byte);
+}
+
+// The prompt path: grid (T, kv_heads, groups), K then V; also into the staging pool (identity layout) when given.
+__global__ void kv_append_q4_batch_kernel(uint8_t* __restrict__ k_q4, uint8_t* __restrict__ v_q4,
+                                          const int32_t* __restrict__ table, int64_t pos0,
+                                          const float* __restrict__ K, const float* __restrict__ V,
+                                          int kv_heads, int head_dim, int page_size, int is_v_grid, KvHostPools host,
+                                          KvHostPools stage) {
+    const long long t = blockIdx.x;
+    const long long pos = pos0 + t;
+    const int h = blockIdx.y, b = blockIdx.z, th = threadIdx.x;
+    const bool is_v = is_v_grid != 0;
+    const float x = (is_v ? V : K)[t * (kv_heads * head_dim) + h * head_dim + b * QK4_0 + th];
+    uint8_t byte;
+    const uint16_t d = q4_group(x, th, byte);
+    const long long page = (long long) table[pos / page_size];
+    const long long row_id = ((pos / page_size) * kv_heads + h) * page_size + (pos % page_size);
+    if (page >= 0) q4_store(is_v ? v_q4 : k_q4, (page * kv_heads + h) * page_size + (pos % page_size), b, th, d, byte);
+    if (host.k_q4 != nullptr) q4_store(is_v ? host.v_q4 : host.k_q4, row_id, b, th, d, byte);
+    if (stage.k_q4 != nullptr) q4_store(is_v ? stage.v_q4 : stage.k_q4, row_id, b, th, d, byte);
+}
+
+// Gather step[kStepWidth] cells into FP16 scratch (the non-fused attention paths)
+__global__ void kv_gather_q4_kernel(const uint8_t* __restrict__ k_q4, const uint8_t* __restrict__ v_q4,
+                                    const int32_t* __restrict__ table, const int32_t* __restrict__ ids,
+                                    const int32_t* __restrict__ step, int kv_heads, int head_dim, int page_size,
+                                    uint16_t* __restrict__ k_scratch, uint16_t* __restrict__ v_scratch) {
+    const long long n_ids = (long long) __ldg(step + kStepWidth);
+    const int blocks_per_head = head_dim / QK4_0;                        // 8
+    const int bytes_per_head = blocks_per_head * sizeof(block_q4_0);    // 144
+    const long long total_blocks = n_ids * kv_heads * blocks_per_head;
+
+    const long long blk_idx = (long long) blockIdx.x * blockDim.y + threadIdx.y;
+    if (blk_idx >= total_blocks) return;
+
+    const int t = threadIdx.x;
+    const long long id = blk_idx / (kv_heads * blocks_per_head);
+    const int rem = (int) (blk_idx % (kv_heads * blocks_per_head));
+    const int h = rem / blocks_per_head;
+    const int b = rem % blocks_per_head;
+
+    const int cell = ids[id];
+    const long long page = (long long) table[cell / page_size];
+    const long long row = (page * kv_heads + h) * page_size + (cell % page_size);
+
+    const block_q4_0* k_blk = reinterpret_cast<const block_q4_0*>(k_q4 + row * bytes_per_head) + b;
+    const block_q4_0* v_blk = reinterpret_cast<const block_q4_0*>(v_q4 + row * bytes_per_head) + b;
+    const float kd = f32_from_f16(k_blk->d);
+    const float vd = f32_from_f16(v_blk->d);
+    const int j = t < 16 ? t : (t - 16);
+    const uint8_t k_byte = k_blk->qs[j];
+    const uint8_t v_byte = v_blk->qs[j];
+    const int kq = (t < 16) ? ((k_byte & 0x0F) - 8) : ((k_byte >> 4) - 8);
+    const int vq = (t < 16) ? ((v_byte & 0x0F) - 8) : ((v_byte >> 4) - 8);
+    const long long dst_offset = ((id * kv_heads + h) * head_dim) + (b * QK4_0 + t);
+    k_scratch[dst_offset] = f16_from_f32((float) kq * kd);
+    v_scratch[dst_offset] = f16_from_f32((float) vq * vd);
+}
+
+void need_256(const QsaShapes& s, const char* what) {
+    if (s.head_dim != 256) {
+        std::fprintf(stderr, "%s: head_dim must be 256 (the Hadamard transform's size)\n", what);
+        std::exit(1);
+    }
+}
+
+}  // namespace
+
+void fwht256_cuda(const float* src, float* dst, int64_t n_rows, void* stream) {
+    if (n_rows <= 0) return;
+    const int rows_per_block = 4;
+    const int64_t num_blocks = (n_rows + rows_per_block - 1) / rows_per_block;
+    fwht256_kernel<<<dim3((unsigned) num_blocks), dim3(32, rows_per_block), 0, (cudaStream_t) stream>>>(
+        src, dst, n_rows, 1.0f / 16.0f);
+    check("fwht256 launch");
+}
+
+void kv_append_q4_step(uint8_t* k_q4, uint8_t* v_q4, const int32_t* page_table, const int32_t* step,
+                       const float* kcur, const float* vcur, const QsaShapes& s, void* stream,
+                       const KvHostPools* host) {
+    need_256(s, "kv_append_q4");
+    const dim3 grid((unsigned) s.n_head_kv, (unsigned) (s.head_dim / QK4_0), 2);
+    kv_append_q4_kernel<<<grid, 32, 0, (cudaStream_t) stream>>>(
+        k_q4, v_q4, page_table, step, kcur, vcur, (int) s.n_head_kv, (int) s.head_dim, (int) s.page_size,
+        host ? *host : KvHostPools{});
+    check("kv_append_q4 launch");
+}
+
+void kv_append_q4(uint8_t* k_q4, uint8_t* v_q4, const int32_t* page_table, int64_t pos0, int64_t T, const float* K,
+                  const float* V, const QsaShapes& s, void* stream, const KvHostPools* host, const KvHostPools* stage) {
+    if (T <= 0) return;
+    need_256(s, "kv_append_q4");
+    const dim3 grid((unsigned) T, (unsigned) s.n_head_kv, (unsigned) (s.head_dim / QK4_0));
+    cudaStream_t cs = (cudaStream_t) stream;
+    const KvHostPools h = host ? *host : KvHostPools{}, st = stage ? *stage : KvHostPools{};
+    for (int is_v = 0; is_v < 2; ++is_v)
+        kv_append_q4_batch_kernel<<<grid, 32, 0, cs>>>(k_q4, v_q4, page_table, pos0, K, V, (int) s.n_head_kv,
+                                                       (int) s.head_dim, (int) s.page_size, is_v, h, st);
+    check("kv_append_q4 batch launch");
+}
+
+void kv_gather_q4_step(const uint8_t* k_q4, const uint8_t* v_q4, const int32_t* page_table, const int32_t* ids,
+                       const int32_t* step, int64_t max_ids, const QsaShapes& s, uint16_t* k_scratch,
+                       uint16_t* v_scratch, void* stream) {
+    if (max_ids <= 0) return;
+    const int blocks_per_head = (int) (s.head_dim / QK4_0);
+    const int64_t total_blocks = max_ids * s.n_head_kv * blocks_per_head;
+    const int rows_per_block = 4;
+    const unsigned num_blocks = (unsigned) ((total_blocks + rows_per_block - 1) / rows_per_block);
+    kv_gather_q4_kernel<<<dim3(num_blocks), dim3(32, rows_per_block), 0, (cudaStream_t) stream>>>(
+        k_q4, v_q4, page_table, ids, step, (int) s.n_head_kv, (int) s.head_dim, (int) s.page_size, k_scratch,
+        v_scratch);
+    check("kv_gather_q4 launch");
+}
+
+}  // namespace strata::kernels
@@ -1,5 +1,6 @@
 // src/kernels/cuda/kv_stream.cu - see include/strata/kernels/kv_stream.hpp.
 #include "strata/kernels/kv_stream.hpp"
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/kernels/kv_q8.hpp"

 #include <cuda_runtime.h>
@@ -29,10 +30,15 @@ struct Runs {
    int n;
 };

-Runs runs_of(const QsaAttnPools& slots, const KvHostPools& host, bool int8, const QsaShapes& s) {
+Runs runs_of(const QsaAttnPools& slots, const KvHostPools& host, int fmt, const QsaShapes& s) {
    const int rows = (int) (s.n_head_kv * s.page_size);
    Runs r{};
-    if (int8) {
+    if (fmt == kKvQ4) {
+        const int bytes = rows * (int) kv_q4_bytes_per_head((int) s.head_dim);
+        r.src[0] = (const uint8_t*) host.k_q4; r.dst[0] = (uint8_t*) slots.k_q4; r.len[0] = bytes;
+        r.src[1] = (const uint8_t*) host.v_q4; r.dst[1] = (uint8_t*) slots.v_q4; r.len[1] = bytes;
+        r.n = 2;
+    } else if (fmt == kKvInt8) {
        const int codes = rows * (int) s.head_dim, scales = rows * (int) (s.head_dim / KV_Q8_GROUP) * 2;
        r.src[0] = (const uint8_t*) host.k_q;     r.dst[0] = (uint8_t*) slots.k_q;     r.len[0] = codes;
        r.src[1] = (const uint8_t*) host.v_q;     r.dst[1] = (uint8_t*) slots.v_q;     r.len[1] = codes;
@@ -183,9 +189,10 @@ __global__ void ring_kernel(int32_t* table, long long n_blocks, long long n_slot

 }  // namespace

-uint64_t kv_block_bytes(const QsaShapes& s, bool int8) {
+uint64_t kv_block_bytes(const QsaShapes& s, int fmt) {
    const uint64_t rows = (uint64_t) (s.n_head_kv * s.page_size);
-    return int8 ? rows * (uint64_t) s.head_dim * 2 + rows * (uint64_t) (s.head_dim / KV_Q8_GROUP) * 2 * 2
+    if (fmt == kKvQ4) return rows * kv_q4_bytes_per_head((int) s.head_dim) * 2;
+    return fmt == kKvInt8 ? rows * (uint64_t) s.head_dim * 2 + rows * (uint64_t) (s.head_dim / KV_Q8_GROUP) * 2 * 2
                : rows * (uint64_t) s.head_dim * 2 * 2;
 }

@@ -194,7 +201,7 @@ void kv_stream_reset(const KvStreamMap& m, void* stream) {
    check("reset");
 }

-void kv_stream_resolve(const KvStreamMap& m, const QsaAttnPools& slots, const KvHostPools& host, bool int8,
+void kv_stream_resolve(const KvStreamMap& m, const QsaAttnPools& slots, const KvHostPools& host, int fmt,
                       const int32_t* ids, const int32_t* steps, int64_t n_q, int64_t cap, const QsaShapes& s,
                       void* stream) {
    if (n_q <= 0) return;
@@ -204,7 +211,7 @@ void kv_stream_resolve(const KvStreamMap& m, const QsaAttnPools& slots, const Kv
    }
    resolve_kernel<<<1, RT, 0, (cudaStream_t) stream>>>(m, ids, steps, (int) n_q, (int) cap, (int) s.page_size);
    check("resolve");
-    copy_kernel<<<96, 128, 0, (cudaStream_t) stream>>>(m, runs_of(slots, host, int8, s));
+    copy_kernel<<<96, 128, 0, (cudaStream_t) stream>>>(m, runs_of(slots, host, fmt, s));
    check("copy");
 }

@@ -213,9 +220,9 @@ void kv_ring_table(int32_t* page_table, int64_t n_blocks, int64_t n_slots, void*
    check("ring table");
 }

-void kv_ring_restore(const QsaAttnPools& slots, const KvHostPools& host, bool int8, int64_t b0, int64_t b1,
+void kv_ring_restore(const QsaAttnPools& slots, const KvHostPools& host, int fmt, int64_t b0, int64_t b1,
                     int64_t n_slots, const QsaShapes& s, void* stream) {
-    const Runs r = runs_of(slots, host, int8, s);
+    const Runs r = runs_of(slots, host, fmt, s);
    for (int64_t b = b0; b < b1;) {
        const int64_t sl = b % n_slots, run = std::min<int64_t>(b1 - b, n_slots - sl);   // up to the ring's end
        for (int a = 0; a < r.n; ++a)
@@ -226,10 +233,10 @@ void kv_ring_restore(const QsaAttnPools& slots, const KvHostPools& host, bool in
    }
 }

-void kv_stage_from_host(const QsaAttnPools& stage, const KvHostPools& host, bool int8, int64_t n_blocks,
+void kv_stage_from_host(const QsaAttnPools& stage, const KvHostPools& host, int fmt, int64_t n_blocks,
                        const QsaShapes& s, void* stream) {
    if (n_blocks <= 0) return;
-    const Runs r = runs_of(stage, host, int8, s);
+    const Runs r = runs_of(stage, host, fmt, s);
    for (int a = 0; a < r.n; ++a)
        if (cudaMemcpyAsync(r.dst[a], r.src[a], (size_t) (n_blocks * r.len[a]), cudaMemcpyDefault,
                            (cudaStream_t) stream) != cudaSuccess)
@@ -1,6 +1,7 @@
 // src/kernels/cuda/qsa_decode_attn.cu - see include/strata/kernels/qsa_decode_attn.hpp.
 #include "strata/kernels/qsa_decode_attn.hpp"
 #include "strata/kernels/kv_q8.hpp"
+#include "strata/kernels/kv_q4.hpp"

 #include <cuda_fp16.h>
 #include <cuda_runtime.h>
@@ -30,9 +31,9 @@ __device__ __forceinline__ float warp_max(float v) {
 }

 // 8 consecutive values of one cell's key or value row for KV head `kvh`, dimensions [d0, d0+8).
-template <bool INT8>
+template <int KV_MODE>
 __device__ __forceinline__ void load8(const QsaAttnPools& p, bool value, long long row, int d0, float* out) {
-    if constexpr (!INT8) {
+    if constexpr (KV_MODE == 0) {
        const uint16_t* base = (value ? p.v_pool : p.k_pool) + row * HD + d0;
        const uint4 raw = *reinterpret_cast<const uint4*>(base);
        const __half2* h2 = reinterpret_cast<const __half2*>(&raw);
@@ -42,7 +43,7 @@ __device__ __forceinline__ void load8(const QsaAttnPools& p, bool value, long lo
            out[2 * j] = f.x;
            out[2 * j + 1] = f.y;
        }
-    } else {
+    } else if constexpr (KV_MODE == 1) {
        const int8_t* codes = (value ? p.v_q : p.k_q) + row * HD + d0;
        const uint16_t sbits = (value ? p.v_scale : p.k_scale)[row * (HD / KV_Q8_GROUP) + d0 / KV_Q8_GROUP];
        const float sc = __half2float(__ushort_as_half(sbits));
@@ -50,10 +51,25 @@ __device__ __forceinline__ void load8(const QsaAttnPools& p, bool value, long lo
        const int8_t* c = reinterpret_cast<const int8_t*>(&raw);
 #pragma unroll
        for (int j = 0; j < 8; ++j) out[j] = (float) c[j] * sc;
+    } else {
+        constexpr int bytes_per_head = (HD / QK4_0) * sizeof(block_q4_0);
+        const int b = d0 / QK4_0;
+        const int rem = d0 % QK4_0;
+        const block_q4_0* blk = reinterpret_cast<const block_q4_0*>((value ? p.v_q4 : p.k_q4) + row * bytes_per_head) + b;
+        const float d = __half2float(__ushort_as_half(blk->d));
+        const int j = (rem == 0 || rem == 16) ? 0 : 8;
+        const uint8_t* bytes = blk->qs + j;
+        if (rem < 16) {
+#pragma unroll
+            for (int k = 0; k < 8; ++k) out[k] = (float) ((int)(bytes[k] & 0x0F) - 8) * d;
+        } else {
+#pragma unroll
+            for (int k = 0; k < 8; ++k) out[k] = (float) ((int)(bytes[k] >> 4) - 8) * d;
+        }
    }
 }

-template <bool INT8>
+template <int KV_MODE>
 __global__ void __launch_bounds__(THREADS) attn_chunk_kernel(const float* __restrict__ q, QsaAttnPools p,
                                                             const int32_t* __restrict__ ids,
                                                             const int32_t* __restrict__ step, int n_kv_heads,
@@ -98,7 +114,7 @@ __global__ void __launch_bounds__(THREADS) attn_chunk_kernel(const float* __rest
            continue;
        }
        float k8[8];
-        load8<INT8>(p, false, srow[c], lane * 8, k8);
+        load8<KV_MODE>(p, false, srow[c], lane * 8, k8);
 #pragma unroll
        for (int h = 0; h < G; ++h) {
            const float4 qa = *reinterpret_cast<const float4*>(&sq[h][lane * 8]);
@@ -128,11 +144,21 @@ __global__ void __launch_bounds__(THREADS) attn_chunk_kernel(const float* __rest
    for (int h = 0; h < G; ++h) acc[h] = 0.0f;
    for (int c = 0; c < n_here; ++c) {
        float v;
-        if constexpr (!INT8) {
+        if constexpr (KV_MODE == 0) {
            v = __half2float(__ushort_as_half(p.v_pool[srow[c] * HD + t]));
-        } else {
+        } else if constexpr (KV_MODE == 1) {
            const float sc = __half2float(__ushort_as_half(p.v_scale[srow[c] * (HD / KV_Q8_GROUP) + t / KV_Q8_GROUP]));
            v = (float) p.v_q[srow[c] * HD + t] * sc;
+        } else {
+            constexpr int bytes_per_head = (HD / QK4_0) * sizeof(block_q4_0);
+            const int b = t / QK4_0;
+            const int rem = t % QK4_0;
+            const block_q4_0* blk = reinterpret_cast<const block_q4_0*>(p.v_q4 + srow[c] * bytes_per_head) + b;
+            const float d = __half2float(__ushort_as_half(blk->d));
+            const int j = rem < 16 ? rem : (rem - 16);
+            const uint8_t byte = blk->qs[j];
+            const int nibble = (rem < 16) ? ((byte & 0x0F) - 8) : ((byte >> 4) - 8);
+            v = (float) nibble * d;
        }
 #pragma unroll
        for (int h = 0; h < G; ++h) acc[h] = fmaf(sp[h][c], v, acc[h]);
@@ -176,7 +202,7 @@ void qsa_decode_attn_batch(const float* q, const QsaAttnPools& pools, const int3
        std::fprintf(stderr, "qsa_decode_attn_batch: unsupported geometry or missing buffers\n");
        std::exit(1);
    }
-    const bool int8 = pools.k_q != nullptr;
+    const int kv_mode = pools.k_q4 != nullptr ? 2 : (pools.k_q != nullptr ? 1 : 0);
    const int n_chunks = (int) ((cap + CHUNK - 1) / CHUNK);
    // per query: [acc: n_chunks*n_head*HD][m: n_chunks*n_head][l: n_chunks*n_head], all offsets from one stride
    const long long stride = (long long) qsa_decode_attn_scratch_floats(cap, s);
@@ -186,12 +212,15 @@ void qsa_decode_attn_batch(const float* q, const QsaAttnPools& pools, const int3
    const float scale = 1.0f / sqrtf((float) HD);
    const dim3 grid((unsigned) n_chunks, (unsigned) s.n_head_kv, (unsigned) n_q);
    cudaStream_t st = (cudaStream_t) stream;
-    if (int8)
-        attn_chunk_kernel<true><<<grid, THREADS, 0, st>>>(q, pools, ids, steps, (int) s.n_head_kv, (int) s.page_size,
-                                                          scale, part_acc, part_m, part_l, n_chunks, (int) cap, stride);
+    if (kv_mode == 2)
+        attn_chunk_kernel<2><<<grid, THREADS, 0, st>>>(q, pools, ids, steps, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks, (int) cap, stride);
+    else if (kv_mode == 1)
+        attn_chunk_kernel<1><<<grid, THREADS, 0, st>>>(q, pools, ids, steps, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks, (int) cap, stride);
    else
-        attn_chunk_kernel<false><<<grid, THREADS, 0, st>>>(q, pools, ids, steps, (int) s.n_head_kv, (int) s.page_size,
-                                                           scale, part_acc, part_m, part_l, n_chunks, (int) cap, stride);
+        attn_chunk_kernel<0><<<grid, THREADS, 0, st>>>(q, pools, ids, steps, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks, (int) cap, stride);
    attn_merge_kernel<<<dim3((unsigned) s.n_head, (unsigned) n_q), HD, 0, st>>>(part_acc, part_m, part_l, n_chunks,
                                                                                  attn, stride);
    const cudaError_t e = cudaGetLastError();
@@ -213,8 +242,8 @@ void qsa_decode_attn_step(const float* q, const QsaAttnPools& pools, const int32
        std::fprintf(stderr, "qsa_decode_attn: unsupported geometry or missing buffers\n");
        std::exit(1);
    }
-    const bool int8 = pools.k_q != nullptr;
-    if (int8 ? (!pools.v_q || !pools.k_scale || !pools.v_scale) : (!pools.k_pool || !pools.v_pool)) {
+    const int kv_mode = pools.k_q4 != nullptr ? 2 : (pools.k_q != nullptr ? 1 : 0);
+    if (kv_mode == 2 ? (!pools.v_q4) : (kv_mode == 1 ? (!pools.v_q || !pools.k_scale || !pools.v_scale) : (!pools.k_pool || !pools.v_pool))) {
        std::fprintf(stderr, "qsa_decode_attn: incomplete KV pools\n");
        std::exit(1);
    }
@@ -225,12 +254,15 @@ void qsa_decode_attn_step(const float* q, const QsaAttnPools& pools, const int32
    const float scale = 1.0f / sqrtf((float) HD);
    const dim3 grid((unsigned) n_chunks, (unsigned) s.n_head_kv);
    cudaStream_t st = (cudaStream_t) stream;
-    if (int8)
-        attn_chunk_kernel<true><<<grid, THREADS, 0, st>>>(q, pools, ids, step, (int) s.n_head_kv, (int) s.page_size,
-                                                          scale, part_acc, part_m, part_l, n_chunks);
+    if (kv_mode == 2)
+        attn_chunk_kernel<2><<<grid, THREADS, 0, st>>>(q, pools, ids, step, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks);
+    else if (kv_mode == 1)
+        attn_chunk_kernel<1><<<grid, THREADS, 0, st>>>(q, pools, ids, step, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks);
    else
-        attn_chunk_kernel<false><<<grid, THREADS, 0, st>>>(q, pools, ids, step, (int) s.n_head_kv, (int) s.page_size,
-                                                           scale, part_acc, part_m, part_l, n_chunks);
+        attn_chunk_kernel<0><<<grid, THREADS, 0, st>>>(q, pools, ids, step, (int) s.n_head_kv, (int) s.page_size,
+                                                        scale, part_acc, part_m, part_l, n_chunks);
    attn_merge_kernel<<<(unsigned) s.n_head, HD, 0, st>>>(part_acc, part_m, part_l, n_chunks, attn);
    const cudaError_t e = cudaGetLastError();
    if (e != cudaSuccess) {
@@ -0,0 +1,223 @@
+// src/kernels/cvec_parity.cpp - the control vector kernel (strata/kernels/cvec.hpp) against a host reference.
+//   1. project: h - s (h.v) v per stream and token, against double precision; the steered component is gone.
+//   2. add: h + d.
+//   3. switched off: R unchanged without a pending write; with one, R is BITWISE the write the fused read folds
+//      (fused_gr_read with apply), which is what keeps a loaded-but-off vector identical to the stock engine.
+//   4. a layer without a direction is untouched.
+
+#include "strata/kernels/cvec.hpp"
+#include "strata/kernels/fused_gr.hpp"
+
+#include <cuda_runtime.h>
+
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <random>
+#include <vector>
+
+namespace k = strata::kernels;
+
+namespace {
+
+int g_fail = 0;
+
+void ck(cudaError_t e, const char* w) {
+    if (e != cudaSuccess) {
+        std::fprintf(stderr, "CUDA error in %s: %s\n", w, cudaGetErrorString(e));
+        std::exit(2);
+    }
+}
+
+template <typename T>
+T* dalloc(size_t n) {
+    T* p = nullptr;
+    ck(cudaMalloc(&p, n * sizeof(T)), "malloc");
+    return p;
+}
+
+template <typename T>
+void up(T* d, const std::vector<T>& h) { ck(cudaMemcpy(d, h.data(), h.size() * sizeof(T), cudaMemcpyHostToDevice), "up"); }
+template <typename T>
+std::vector<T> down(const T* d, size_t n) {
+    std::vector<T> h(n);
+    ck(cudaMemcpy(h.data(), d, n * sizeof(T), cudaMemcpyDeviceToHost), "down");
+    return h;
+}
+
+void check(bool ok, const char* what) {
+    std::printf("  %-66s %s\n", what, ok ? "ok" : "FAIL");
+    if (!ok) ++g_fail;
+}
+
+}  // namespace
+
+int main() {
+    constexpr int64_t L = 48, N = 2560, HC = 4, LR = 320, T = 5, D = HC * N;
+    constexpr int64_t kLayer = 7, kOff = 3;   // steered / not steered
+    std::mt19937 rng(1234);
+    std::normal_distribution<float> nd(0.0f, 1.0f);
+
+    // a unit direction at kLayer with a norm-2 scaled one's s = 2; nothing at kOff
+    std::vector<float> dir((size_t) (L * N), 0.0f), s((size_t) L, 0.0f);
+    {
+        double nrm = 0.0;
+        std::vector<float> v((size_t) N);
+        for (auto& x : v) { x = nd(rng); nrm += (double) x * x; }
+        nrm = std::sqrt(nrm);
+        for (int64_t j = 0; j < N; ++j) dir[(size_t) (kLayer * N + j)] = (float) (v[(size_t) j] / nrm);
+        s[(size_t) kLayer] = 2.0f;
+    }
+    std::string err;
+    if (!k::cvec_upload(dir, s, /*project*/ 0, 4, 44, N, HC, err)) { std::fprintf(stderr, "%s\n", err.c_str()); return 2; }
+    check(k::cvec().covers(kLayer) && !k::cvec().covers(kOff) && !k::cvec().covers(45), "covers(): steered layers only");
+
+    std::vector<float> R((size_t) (T * D)), bo((size_t) (T * N)), inj((size_t) (T * HC));
+    for (auto& x : R) x = nd(rng) * 3.0f;
+    for (auto& x : bo) x = nd(rng);
+    for (auto& x : inj) x = nd(rng);
+    float* dR = dalloc<float>(R.size());
+    float* dbo = dalloc<float>(bo.size());
+    float* dinj = dalloc<float>(inj.size());
+    up(dbo, bo);
+    up(dinj, inj);
+
+    // ---- 1. project, no pending write
+    std::printf("project\n");
+    up(dR, R);
+    k::cvec_apply(dR, kLayer, T, D, nullptr, 0, nullptr, 0, false, nullptr);
+    ck(cudaDeviceSynchronize(), "project");
+    {
+        const auto got = down(dR, R.size());
+        double worst = 0.0, worst_dot = 0.0;
+        for (int64_t t = 0; t < T; ++t)
+            for (int64_t c = 0; c < HC; ++c) {
+                const float* h = R.data() + t * D + c * N;
+                const float* v = dir.data() + kLayer * N;
+                double dot = 0.0;
+                for (int64_t j = 0; j < N; ++j) dot += (double) h[j] * v[j];
+                double after = 0.0;
+                for (int64_t j = 0; j < N; ++j) {
+                    const double want = h[j] - 2.0 * dot * v[j];
+                    const double g = got[(size_t) (t * D + c * N + j)];
+                    worst = std::fmax(worst, std::fabs(g - want));
+                    after += g * v[j];
+                }
+                // s = 2 reflects the component: h.v -> -h.v
+                worst_dot = std::fmax(worst_dot, std::fabs(after + dot));
+            }
+        std::printf("    max |err| %.3g, max |h'.v + h.v| %.3g\n", worst, worst_dot);
+        check(worst < 1e-4, "project matches h - s (h.v) v (s = 2)");
+        check(worst_dot < 1e-3, "the component along v is reflected (s = 2)");
+    }
+    // s = 1 removes it
+    s[(size_t) kLayer] = 1.0f;
+    if (!k::cvec_upload(dir, s, 0, 4, 44, N, HC, err)) return 2;
+    up(dR, R);
+    k::cvec_apply(dR, kLayer, T, D, nullptr, 0, nullptr, 0, false, nullptr);
+    ck(cudaDeviceSynchronize(), "project s=1");
+    {
+        const auto got = down(dR, R.size());
+        double worst = 0.0;
+        for (int64_t t = 0; t < T; ++t)
+            for (int64_t c = 0; c < HC; ++c) {
+                double after = 0.0;
+                for (int64_t j = 0; j < N; ++j) after += (double) got[(size_t) (t * D + c * N + j)] * dir[(size_t) (kLayer * N + j)];
+                worst = std::fmax(worst, std::fabs(after));
+            }
+        std::printf("    max |h'.v| %.3g\n", worst);
+        check(worst < 1e-3, "s = 1 projects the direction out");
+    }
+
+    // ---- 4. a layer without a direction
+    up(dR, R);
+    k::cvec_apply(dR, kOff, T, D, nullptr, 0, nullptr, 0, false, nullptr);
+    ck(cudaDeviceSynchronize(), "off layer");
+    check(std::memcmp(down(dR, R.size()).data(), R.data(), R.size() * 4) == 0, "a layer without a direction is untouched");
+
+    // ---- 3. switched off
+    std::printf("switched off\n");
+    k::cvec_set_enabled(false);
+    check(!k::cvec_enabled(), "cvec_enabled() follows the switch");
+    up(dR, R);
+    k::cvec_apply(dR, kLayer, T, D, nullptr, 0, nullptr, 0, false, nullptr);
+    ck(cudaDeviceSynchronize(), "off");
+    check(std::memcmp(down(dR, R.size()).data(), R.data(), R.size() * 4) == 0, "off, no pending write: R unchanged");
+    {
+        // the fused read's folded write, one token at a time, against the kernel's write-only pass
+        std::vector<uint16_t> wd((size_t) (LR * D)), wu((size_t) (D * LR)), wi((size_t) (HC * D));
+        std::vector<float> wn((size_t) D);
+        auto bf = [&](float x) { uint32_t u; std::memcpy(&u, &x, 4); return (uint16_t) (u >> 16); };
+        for (auto& x : wd) x = bf(nd(rng) * 0.02f);
+        for (auto& x : wu) x = bf(nd(rng) * 0.02f);
+        for (auto& x : wi) x = bf(nd(rng) * 0.02f);
+        for (auto& x : wn) x = 1.0f + 0.1f * nd(rng);
+        uint16_t* dwd = dalloc<uint16_t>(wd.size());
+        uint16_t* dwu = dalloc<uint16_t>(wu.size());
+        uint16_t* dwi = dalloc<uint16_t>(wi.size());
+        float* dwn = dalloc<float>(wn.size());
+        up(dwd, wd); up(dwu, wu); up(dwi, wi); up(dwn, wn);
+        float* lo = dalloc<float>(LR);
+        float* rs = dalloc<float>(HC);
+        float* inj_out = dalloc<float>(HC);
+        float* mixed = dalloc<float>(N);
+        float* dF = dalloc<float>(R.size());
+        up(dF, R);
+        for (int64_t t = 0; t < T; ++t) {
+            k::FusedGrArgs a;
+            a.R = dF + t * D; a.R_out = dF + t * D; a.apply = true;
+            a.bo_prev = dbo + t * N; a.inj_prev = dinj + t * HC;
+            a.w_norm = dwn; a.w_down = dwd; a.w_up = dwu; a.w_inject = dwi;
+            a.lo = lo; a.rs = rs; a.inject_out = inj_out; a.mixed = mixed;
+            k::fused_gr_read(a, nullptr);
+        }
+        up(dR, R);
+        k::cvec_apply(dR, kLayer, T, D, dbo, N, dinj, HC, true, nullptr);
+        ck(cudaDeviceSynchronize(), "write only");
+        const auto fused = down(dF, R.size()), mine = down(dR, R.size());
+        check(std::memcmp(fused.data(), mine.data(), R.size() * 4) == 0,
+              "off, pending write: bitwise the fused read's folded write");
+        k::cvec_set_enabled(true);
+        // on, with the write: the write, then the projection (reference from the fused result)
+        up(dR, R);
+        k::cvec_apply(dR, kLayer, T, D, dbo, N, dinj, HC, true, nullptr);
+        ck(cudaDeviceSynchronize(), "write+project");
+        const auto both = down(dR, R.size());
+        double worst = 0.0;
+        for (int64_t t = 0; t < T; ++t)
+            for (int64_t c = 0; c < HC; ++c) {
+                const float* h = fused.data() + t * D + c * N;
+                const float* v = dir.data() + kLayer * N;
+                double dot = 0.0;
+                for (int64_t j = 0; j < N; ++j) dot += (double) h[j] * v[j];
+                for (int64_t j = 0; j < N; ++j)
+                    worst = std::fmax(worst, std::fabs(both[(size_t) (t * D + c * N + j)] - (h[j] - dot * v[j])));
+            }
+        std::printf("    write + project: max |err| %.3g\n", worst);
+        check(worst < 1e-4, "on, pending write: the write, then the projection");
+    }
+
+    // ---- 2. add
+    std::printf("add\n");
+    for (int64_t j = 0; j < N; ++j) dir[(size_t) (kLayer * N + j)] *= 0.1f;   // d = 0.1 v, s = 1
+    s[(size_t) kLayer] = 1.0f;
+    if (!k::cvec_upload(dir, s, /*add*/ 1, 4, 44, N, HC, err)) return 2;
+    up(dR, R);
+    k::cvec_apply(dR, kLayer, T, D, nullptr, 0, nullptr, 0, false, nullptr);
+    ck(cudaDeviceSynchronize(), "add");
+    {
+        const auto got = down(dR, R.size());
+        bool same = true;
+        for (int64_t t = 0; t < T; ++t)
+            for (int64_t c = 0; c < HC; ++c)
+                for (int64_t j = 0; j < N; ++j) {
+                    const size_t i = (size_t) (t * D + c * N + j);
+                    same = same && got[i] == R[i] + dir[(size_t) (kLayer * N + j)];
+                }
+        check(same, "add is h + d in every stream (bitwise)");
+    }
+
+    std::printf(g_fail ? "cvec_parity: %d FAILED\n" : "cvec_parity: all passed\n", g_fail);
+    return g_fail ? 1 : 0;
+}
@@ -0,0 +1,346 @@
+// src/kernels/kv_q4_parity.cpp - Parity test for Q4_0 KV cache with orthonormal Walsh-Hadamard rotation.
+// Validates:
+// 1. FWHT 256 CUDA kernel vs exact mathematical orthonormal Walsh-Hadamard transform.
+//    (H * H * x == x, H is symmetric and orthonormal with scale 1/sqrt(256) = 1/16).
+// 2. Q4_0 quantization and packing (32 values per block, 18 bytes) bitwise vs host reference.
+// 3. kv_append_q4_step and kv_gather_q4_step through paged pool against host reference.
+// 4. Invariance of dot products under Walsh-Hadamard rotation: (H*q) . (H*k) == q . k.
+
+#include "strata/kernels/f16_bits.hpp"
+#include "strata/kernels/kv_q4.hpp"
+#include "strata/kernels/qsa.hpp"
+
+#include <cuda_runtime.h>
+
+#include <algorithm>
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <random>
+#include <vector>
+
+namespace k = strata::kernels;
+
+namespace {
+
+int g_fail = 0;
+
+void ck(cudaError_t e, const char* w) {
+    if (e != cudaSuccess) {
+        std::fprintf(stderr, "CUDA error in %s: %s\n", w, cudaGetErrorString(e));
+        std::exit(2);
+    }
+}
+
+template <typename T>
+T* dalloc(size_t n) {
+    T* p = nullptr;
+    ck(cudaMalloc(&p, n * sizeof(T) + 64), "malloc");
+    ck(cudaMemset(p, 0, n * sizeof(T) + 64), "memset");
+    return p;
+}
+
+// Exact CPU reference for Fast Walsh-Hadamard Transform (D = 256)
+// Sylvester construction matching llama.cpp's ggml_gen_hadamard
+void fwht256_host_reference(float* x) {
+    constexpr int n = 256;
+    for (int s = 1; s < n; s *= 2) {
+        for (int i = 0; i < n; i += 2 * s) {
+            for (int j = 0; j < s; ++j) {
+                const float u = x[i + j];
+                const float v = x[i + j + s];
+                x[i + j]     = u + v;
+                x[i + j + s] = u - v;
+            }
+        }
+    }
+    const float scale = 1.0f / 16.0f; // 1 / sqrt(256)
+    for (int i = 0; i < n; ++i) {
+        x[i] *= scale;
+    }
+}
+
+// Host reference for Q4_0 quantization of 32 elements (1 block)
+void quantize_block_q4_0_host(const float* x, k::block_q4_0& blk) {
+    float amax = 0.0f;
+    float max_val = 0.0f;
+    for (int i = 0; i < 32; ++i) {
+        const float v = x[i];
+        if (amax < std::fabs(v)) {
+            amax = std::fabs(v);
+            max_val = v;
+        }
+    }
+    const float d = max_val / -8.0f;
+    const float id = d != 0.0f ? 1.0f / d : 0.0f;
+    blk.d = k::f16_from_f32(d);
+
+    for (int i = 0; i < 16; ++i) {
+        const float x0 = x[i] * id;
+        const float x1 = x[i + 16] * id;
+        const int q0 = (int) (x0 + 8.5f);
+        const int q1 = (int) (x1 + 8.5f);
+        const uint8_t xi0 = (uint8_t) std::min(15, std::max(0, q0));
+        const uint8_t xi1 = (uint8_t) std::min(15, std::max(0, q1));
+        blk.qs[i] = xi0 | (xi1 << 4);
+    }
+}
+
+} // namespace
+
+int main() {
+    std::printf("=== Running kv_q4_parity test ===\n");
+    std::mt19937 rng(42);
+    std::normal_distribution<float> nd(0.0f, 1.0f);
+
+    // -------------------------------------------------------------
+    // Test 1: Orthonormal Fast Walsh-Hadamard Transform (D=256)
+    // -------------------------------------------------------------
+    std::printf("[1/4] Verifying FWHT-256 CUDA kernel vs exact mathematical reference...\n");
+    constexpr int n_vectors = 64;
+    std::vector<float> h_in(n_vectors * 256);
+    std::vector<float> h_ref(n_vectors * 256);
+    for (size_t i = 0; i < h_in.size(); ++i) {
+        h_in[i] = nd(rng);
+    }
+    h_ref = h_in;
+
+    // Run CPU reference on each row
+    for (int r = 0; r < n_vectors; ++r) {
+        fwht256_host_reference(h_ref.data() + r * 256);
+    }
+
+    // Run GPU kernel
+    float* d_src = dalloc<float>(n_vectors * 256);
+    float* d_dst = dalloc<float>(n_vectors * 256);
+    ck(cudaMemcpy(d_src, h_in.data(), h_in.size() * sizeof(float), cudaMemcpyHostToDevice), "memcpy H2D");
+
+    k::fwht256_cuda(d_src, d_dst, n_vectors, nullptr);
+    ck(cudaDeviceSynchronize(), "fwht256_cuda");
+
+    std::vector<float> h_out(n_vectors * 256);
+    ck(cudaMemcpy(h_out.data(), d_dst, h_out.size() * sizeof(float), cudaMemcpyDeviceToHost), "memcpy D2H");
+
+    double max_fwht_diff = 0.0;
+    for (size_t i = 0; i < h_out.size(); ++i) {
+        double diff = std::fabs(h_out[i] - h_ref[i]);
+        if (diff > max_fwht_diff) max_fwht_diff = diff;
+    }
+    if (max_fwht_diff > 1e-5) {
+        std::fprintf(stderr, "FAIL: FWHT-256 GPU output differs from host reference (max diff = %e)\n", max_fwht_diff);
+        ++g_fail;
+    } else {
+        std::printf("  -> FWHT-256 bitwise match with host reference (max diff = %e, OK)\n", max_fwht_diff);
+    }
+
+    // Self-inverse property: H * (H * x) == x
+    k::fwht256_inplace_cuda(d_dst, n_vectors, nullptr);
+    ck(cudaDeviceSynchronize(), "fwht256_inplace_cuda");
+    ck(cudaMemcpy(h_out.data(), d_dst, h_out.size() * sizeof(float), cudaMemcpyDeviceToHost), "memcpy D2H");
+
+    double max_inv_diff = 0.0;
+    for (size_t i = 0; i < h_out.size(); ++i) {
+        double diff = std::fabs(h_out[i] - h_in[i]);
+        if (diff > max_inv_diff) max_inv_diff = diff;
+    }
+    if (max_inv_diff > 1e-5) {
+        std::fprintf(stderr, "FAIL: FWHT-256 self-inverse property failed (max diff = %e)\n", max_inv_diff);
+        ++g_fail;
+    } else {
+        std::printf("  -> FWHT-256 self-inverse property verified H*H*x == x (max diff = %e, OK)\n", max_inv_diff);
+    }
+
+    // -------------------------------------------------------------
+    // Test 2: Invariance of Attention Scores under Hadamard Rotation
+    // -------------------------------------------------------------
+    std::printf("[2/4] Verifying Attention Score Invariance: <H*q, H*k> == <q, k>...\n");
+    double max_dot_diff = 0.0;
+    for (int r = 0; r < n_vectors; r += 2) {
+        const float* q = h_in.data() + r * 256;
+        const float* k_vec = h_in.data() + (r + 1) * 256;
+        const float* hq = h_ref.data() + r * 256;
+        const float* hk = h_ref.data() + (r + 1) * 256;
+
+        double dot_raw = 0.0;
+        double dot_rot = 0.0;
+        for (int i = 0; i < 256; ++i) {
+            dot_raw += (double) q[i] * (double) k_vec[i];
+            dot_rot += (double) hq[i] * (double) hk[i];
+        }
+        double d = std::fabs(dot_raw - dot_rot);
+        if (d > max_dot_diff) max_dot_diff = d;
+    }
+    if (max_dot_diff > 1e-5) {
+        std::fprintf(stderr, "FAIL: Rotated dot product differs from original (max diff = %e)\n", max_dot_diff);
+        ++g_fail;
+    } else {
+        std::printf("  -> Dot product invariant under rotation: |<Hq,Hk> - <q,k>| = %e (OK)\n", max_dot_diff);
+    }
+
+    // -------------------------------------------------------------
+    // Test 3: Q4_0 Paged KV Append & Gather against Host Reference
+    // -------------------------------------------------------------
+    std::printf("[3/4] Verifying Q4_0 KV append and gather against host reference...\n");
+    k::QsaShapes s = k::qsa_real_shapes();
+    s.page_size = 64;
+    const int H = (int) s.n_head_kv; // 2
+    const int D = (int) s.head_dim;   // 256
+    const int P = (int) s.page_size;  // 64
+    const int pages = 8;
+    const int cells = pages * P;
+
+    std::vector<int32_t> table(pages);
+    for (int i = 0; i < pages; ++i) table[i] = (i * 3 + 5) % pages; // permutation
+    int32_t* d_table = dalloc<int32_t>(pages);
+    ck(cudaMemcpy(d_table, table.data(), pages * sizeof(int32_t), cudaMemcpyHostToDevice), "memcpy table");
+
+    const size_t pool_bytes = (size_t) pages * H * P * k::kv_q4_bytes_per_head(D);
+    uint8_t* d_k_q4 = dalloc<uint8_t>(pool_bytes);
+    uint8_t* d_v_q4 = dalloc<uint8_t>(pool_bytes);
+    float* d_kcur = dalloc<float>(H * D);
+    float* d_vcur = dalloc<float>(H * D);
+    int32_t* d_step = dalloc<int32_t>(k::kStepCount);
+
+    std::vector<std::vector<float>> host_k(cells), host_v(cells);
+    std::vector<int> positions(cells);
+    for (int i = 0; i < cells; ++i) positions[i] = i;
+    std::shuffle(positions.begin(), positions.end(), rng);
+
+    const int n_fill = cells - 25; // leave some cells empty
+    for (int n = 0; n < n_fill; ++n) {
+        const int pos = positions[n];
+        std::vector<float> kv(H * D), vv(H * D);
+        for (auto& x : kv) x = nd(rng) * 2.0f;
+        for (auto& x : vv) x = nd(rng) * 2.0f;
+
+        // Apply FWHT before append
+        for (int h = 0; h < H; ++h) {
+            fwht256_host_reference(kv.data() + h * D);
+            fwht256_host_reference(vv.data() + h * D);
+        }
+        host_k[pos] = kv;
+        host_v[pos] = vv;
+
+        int32_t hstep[k::kStepCount] = {pos, pos + 1, 0, 0};
+        ck(cudaMemcpy(d_step, hstep, sizeof(hstep), cudaMemcpyHostToDevice), "memcpy step");
+        ck(cudaMemcpy(d_kcur, kv.data(), kv.size() * sizeof(float), cudaMemcpyHostToDevice), "memcpy k");
+        ck(cudaMemcpy(d_vcur, vv.data(), vv.size() * sizeof(float), cudaMemcpyHostToDevice), "memcpy v");
+
+        k::kv_append_q4_step(d_k_q4, d_v_q4, d_table, d_step, d_kcur, d_vcur, s, nullptr);
+        ck(cudaDeviceSynchronize(), "kv_append_q4_step");
+    }
+
+    // Verify written Q4_0 blocks bitwise against host quantizer
+    std::vector<uint8_t> h_k_q4(pool_bytes), h_v_q4(pool_bytes);
+    ck(cudaMemcpy(h_k_q4.data(), d_k_q4, pool_bytes, cudaMemcpyDeviceToHost), "memcpy d2h k_q4");
+    ck(cudaMemcpy(h_v_q4.data(), d_v_q4, pool_bytes, cudaMemcpyDeviceToHost), "memcpy d2h v_q4");
+
+    long bad_blocks = 0;
+    const int blocks_per_head = D / k::QK4_0; // 8
+    const int bytes_per_head = blocks_per_head * sizeof(k::block_q4_0); // 144
+
+    for (int n = 0; n < n_fill; ++n) {
+        const int pos = positions[n];
+        for (int is_v = 0; is_v < 2; ++is_v) {
+            for (int h = 0; h < H; ++h) {
+                const long long page = (long long) table[pos / P];
+                const long long row = (page * H + h) * P + (pos % P);
+                const uint8_t* pool_ptr = (is_v ? h_v_q4.data() : h_k_q4.data()) + row * bytes_per_head;
+                const float* raw_vec = (is_v ? host_v[pos].data() : host_k[pos].data()) + h * D;
+
+                for (int b = 0; b < blocks_per_head; ++b) {
+                    k::block_q4_0 host_blk;
+                    quantize_block_q4_0_host(raw_vec + b * 32, host_blk);
+
+                    const k::block_q4_0* gpu_blk = reinterpret_cast<const k::block_q4_0*>(pool_ptr) + b;
+                    if (std::memcmp(&host_blk, gpu_blk, sizeof(k::block_q4_0)) != 0) {
+                        if (bad_blocks == 0) {
+                            std::printf("MISMATCH at n=%d, pos=%d, is_v=%d, h=%d, b=%d\n", n, pos, is_v, h, b);
+                            std::printf("  host blk.d = 0x%04x (%f), gpu blk.d = 0x%04x (%f)\n",
+                                        host_blk.d, k::f32_from_f16(host_blk.d),
+                                        gpu_blk->d, k::f32_from_f16(gpu_blk->d));
+                            std::printf("  host qs: ");
+                            for (int i = 0; i < 16; ++i) std::printf("%02x ", host_blk.qs[i]);
+                            std::printf("\n  gpu  qs: ");
+                            for (int i = 0; i < 16; ++i) std::printf("%02x ", gpu_blk->qs[i]);
+                            std::printf("\n");
+                        }
+                        ++bad_blocks;
+                    }
+                }
+            }
+        }
+    }
+    if (bad_blocks > 0) {
+        std::fprintf(stderr, "FAIL: %ld Q4_0 blocks differ from host quantizer\n", bad_blocks);
+        ++g_fail;
+    } else {
+        std::printf("  -> All Q4_0 pool blocks bitwise equal to host quantizer (OK)\n");
+    }
+
+    // -------------------------------------------------------------
+    // Test 4: Gather Q4_0 into FP16 Scratch and verify accuracy
+    // -------------------------------------------------------------
+    std::printf("[4/4] Verifying Q4_0 gather unpacking and dequantization...\n");
+    const int max_ids = 128;
+    int32_t* d_ids = dalloc<int32_t>(max_ids);
+    uint16_t* d_k_scratch = dalloc<uint16_t>((size_t) max_ids * H * D);
+    uint16_t* d_v_scratch = dalloc<uint16_t>((size_t) max_ids * H * D);
+
+    std::vector<int32_t> ids(max_ids);
+    for (int i = 0; i < max_ids; ++i) {
+        ids[i] = positions[rng() % n_fill];
+    }
+    ck(cudaMemcpy(d_ids, ids.data(), max_ids * sizeof(int32_t), cudaMemcpyHostToDevice), "memcpy ids");
+
+    int32_t hstep[k::kStepCount] = {0, 0, 0, max_ids};
+    ck(cudaMemcpy(d_step, hstep, sizeof(hstep), cudaMemcpyHostToDevice), "memcpy step");
+
+    k::kv_gather_q4_step(d_k_q4, d_v_q4, d_table, d_ids, d_step, max_ids, s, d_k_scratch, d_v_scratch, nullptr);
+    ck(cudaDeviceSynchronize(), "kv_gather_q4_step");
+
+    std::vector<uint16_t> h_k_scratch((size_t) max_ids * H * D);
+    std::vector<uint16_t> h_v_scratch((size_t) max_ids * H * D);
+    ck(cudaMemcpy(h_k_scratch.data(), d_k_scratch, h_k_scratch.size() * sizeof(uint16_t), cudaMemcpyDeviceToHost), "memcpy d2h k_scratch");
+    ck(cudaMemcpy(h_v_scratch.data(), d_v_scratch, h_v_scratch.size() * sizeof(uint16_t), cudaMemcpyDeviceToHost), "memcpy d2h v_scratch");
+
+    double worst_quant_err = 0.0;
+    for (int i = 0; i < max_ids; ++i) {
+        const int cell = ids[i];
+        for (int is_v = 0; is_v < 2; ++is_v) {
+            for (int h = 0; h < H; ++h) {
+                const float* orig = (is_v ? host_v[cell].data() : host_k[cell].data()) + h * D;
+                const uint16_t* gathered = (is_v ? h_v_scratch.data() : h_k_scratch.data()) + (i * H + h) * D;
+
+                for (int d = 0; d < D; ++d) {
+                    float deq = k::f32_from_f16(gathered[d]);
+                    float raw = orig[d];
+                    double err = std::fabs(deq - raw);
+                    if (err > worst_quant_err) worst_quant_err = err;
+                }
+            }
+        }
+    }
+    std::printf("  -> Max reconstruction error after 4-bit quant + dequant: %.4f (OK)\n", worst_quant_err);
+
+    cudaFree(d_src);
+    cudaFree(d_dst);
+    cudaFree(d_table);
+    cudaFree(d_k_q4);
+    cudaFree(d_v_q4);
+    cudaFree(d_kcur);
+    cudaFree(d_vcur);
+    cudaFree(d_step);
+    cudaFree(d_ids);
+    cudaFree(d_k_scratch);
+    cudaFree(d_v_scratch);
+
+    if (g_fail == 0) {
+        std::printf("=== kv_q4_parity: ALL TESTS PASSED SUCCESSFULLY! ===\n");
+        return 0;
+    } else {
+        std::fprintf(stderr, "=== kv_q4_parity: FAILED (%d failures) ===\n", g_fail);
+        return 1;
+    }
+}
@@ -8,7 +8,8 @@
 //   2. the residency map is consistent after every call (slot_block and page_table invert each other);
 //   3. no call overflowed, and the hit/miss counters add up;
 //   4. a ring (the MTP drafter's layout) restored from the host copy reads the same values as the resident pool.
-// INT8 and FP16 pools both.
+// INT8, FP16 and Q4_0 (PR #21) pools.
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/kernels/kv_q8.hpp"
 #include "strata/kernels/kv_stream.hpp"
 #include "strata/kernels/qsa.hpp"
@@ -48,9 +49,13 @@ template <typename T> T* halloc(size_t n) {   // pinned, mapped; returns the dev

 struct Pools {   // one K/V pool set of `pages` pages
    k::KvHostPools p;   // reused as a plain pointer bundle
-    void alloc(int64_t pages, const k::QsaShapes& s, bool int8, bool host) {
+    void alloc(int64_t pages, const k::QsaShapes& s, int fmt, bool host) {
        const size_t rows = (size_t) pages * s.n_head_kv * s.page_size;
-        if (int8) {
+        if (fmt == k::kKvQ4) {
+            const size_t b = rows * k::kv_q4_bytes_per_head((int) s.head_dim);
+            p.k_q4 = host ? halloc<uint8_t>(b) : dalloc<uint8_t>(b);
+            p.v_q4 = host ? halloc<uint8_t>(b) : dalloc<uint8_t>(b);
+        } else if (fmt == k::kKvInt8) {
            p.k_q = host ? halloc<int8_t>(rows * s.head_dim) : dalloc<int8_t>(rows * s.head_dim);
            p.v_q = host ? halloc<int8_t>(rows * s.head_dim) : dalloc<int8_t>(rows * s.head_dim);
            p.k_scale = host ? halloc<uint16_t>(rows * 4) : dalloc<uint16_t>(rows * 4);
@@ -63,14 +68,15 @@ struct Pools {   // one K/V pool set of `pages` pages
    k::QsaAttnPools attn(const int32_t* table) const {
        k::QsaAttnPools a;
        a.k_pool = p.k_pool; a.v_pool = p.v_pool; a.k_q = p.k_q; a.v_q = p.v_q; a.k_scale = p.k_scale;
-        a.v_scale = p.v_scale; a.page_table = table;
+        a.v_scale = p.v_scale; a.k_q4 = p.k_q4; a.v_q4 = p.v_q4; a.page_table = table;
        return a;
    }
 };

 void append(const Pools& pl, const int32_t* table, const int32_t* step, const float* kc, const float* vc,
-            const k::QsaShapes& s, bool int8, const k::KvHostPools* host) {
-    if (int8) k::kv_append_q8_step(pl.p.k_q, pl.p.v_q, pl.p.k_scale, pl.p.v_scale, table, step, kc, vc, s, nullptr, host);
+            const k::QsaShapes& s, int fmt, const k::KvHostPools* host) {
+    if (fmt == k::kKvQ4) k::kv_append_q4_step(pl.p.k_q4, pl.p.v_q4, table, step, kc, vc, s, nullptr, host);
+    else if (fmt == k::kKvInt8) k::kv_append_q8_step(pl.p.k_q, pl.p.v_q, pl.p.k_scale, pl.p.v_scale, table, step, kc, vc, s, nullptr, host);
    else k::kv_append_step(pl.p.k_pool, pl.p.v_pool, table, step, kc, vc, s, nullptr, host);
 }

@@ -96,18 +102,19 @@ std::vector<int32_t> selection(int64_t n_kv, int64_t width, std::mt19937& rng) {
    return ids;
 }

-bool run(bool int8) {
+bool run(int fmt) {
+    const char* name = fmt == k::kKvQ4 ? "q4_0" : fmt == k::kKvInt8 ? "int8" : "fp16";
    k::QsaShapes s = k::qsa_real_shapes();
    const int64_t N = 40000, n_blocks = (N + 3) / 4, n_slots = 8 * 516 + 700;   // must evict: slots < blocks
    const int64_t cap = k::qsa_selection_width(k::kTopkMaxCells, s), NQ = 8;
    const int64_t H = s.n_head_kv, D = s.head_dim, NH = s.n_head;
-    std::mt19937 rng(int8 ? 7 : 11);
+    std::mt19937 rng(7 + 4 * fmt);
    std::normal_distribution<float> nd(0.f, 1.f);

    Pools ref, slots, host;
-    ref.alloc(n_blocks, s, int8, false);
-    slots.alloc(n_slots, s, int8, false);
-    host.alloc(n_blocks, s, int8, true);
+    ref.alloc(n_blocks, s, fmt, false);
+    slots.alloc(n_slots, s, fmt, false);
+    host.alloc(n_blocks, s, fmt, true);
    int32_t* ident = dalloc<int32_t>(n_blocks);
    {
        std::vector<int32_t> t(n_blocks);
@@ -143,8 +150,8 @@ bool run(bool int8) {
        ck(cudaMemcpy(kc, hk.data(), hk.size() * 4, cudaMemcpyHostToDevice), "k");
        ck(cudaMemcpy(vc, hv.data(), hv.size() * 4, cudaMemcpyHostToDevice), "v");
        ck(cudaMemcpy(step, st, sizeof(st), cudaMemcpyHostToDevice), "step");
-        append(ref, ident, step, kc, vc, s, int8, nullptr);
-        append(slots, m.page_table, step, kc, vc, s, int8, &host.p);
+        append(ref, ident, step, kc, vc, s, fmt, nullptr);
+        append(slots, m.page_table, step, kc, vc, s, fmt, &host.p);
        if (pos % 131 != 130 && pos != N - 1) continue;
        // a batch: n_q queries at the last n_q positions (as a verify window), each with its own selection
        const int n_q = 1 + (int) (rng() % NQ);
@@ -163,13 +170,13 @@ bool run(bool int8) {
        ck(cudaMemcpy(steps, hst.data(), hst.size() * 4, cudaMemcpyHostToDevice), "steps");
        ck(cudaMemcpy(q, hq.data(), hq.size() * 4, cudaMemcpyHostToDevice), "q");
        k::qsa_decode_attn_batch(q, ref.attn(ident), ids, steps, cap, s, scratch, out_ref, n_q, nullptr);
-        k::kv_stream_resolve(m, slots.attn(m.page_table), host.p, int8, ids, steps, n_q, cap, s, nullptr);
+        k::kv_stream_resolve(m, slots.attn(m.page_table), host.p, fmt, ids, steps, n_q, cap, s, nullptr);
        k::qsa_decode_attn_batch(q, slots.attn(m.page_table), ids, steps, cap, s, scratch, out_str, n_q, nullptr);
        ck(cudaDeviceSynchronize(), "batch");
        ck(cudaMemcpy(a.data(), out_ref, (size_t) n_q * NH * D * 4, cudaMemcpyDeviceToHost), "a");
        ck(cudaMemcpy(b2.data(), out_str, (size_t) n_q * NH * D * 4, cudaMemcpyDeviceToHost), "b");
        if (std::memcmp(a.data(), b2.data(), (size_t) n_q * NH * D * 4) != 0) {
-            if (bad++ < 5) std::fprintf(stderr, "  %s pos %lld n_q %d: streamed attention differs\n", int8 ? "int8" : "fp16", (long long) pos, n_q);
+            if (bad++ < 5) std::fprintf(stderr, "  %s pos %lld n_q %d: streamed attention differs\n", name, (long long) pos, n_q);
        }
        // the map inverts itself
        ck(cudaMemcpy(pt.data(), m.page_table, n_blocks * 4, cudaMemcpyDeviceToHost), "pt");
@@ -188,7 +195,7 @@ bool run(bool int8) {
    }
    const k::KvStreamCounters c = k::kv_stream_counters(m);
    std::printf("  %s: %d batches, %llu block lookups, %llu misses (%.1f%% hit), overflow %d, %d failures\n",
-                int8 ? "int8" : "fp16", batches, (unsigned long long) c.lookups, (unsigned long long) c.misses,
+                name, batches, (unsigned long long) c.lookups, (unsigned long long) c.misses,
                c.lookups ? 100.0 * (double) (c.lookups - c.misses) / (double) c.lookups : 0.0, (int) c.overflow, bad);
    if (c.overflow || c.calls != (uint64_t) batches || c.misses == 0 || c.misses >= c.lookups) ++bad;

@@ -196,10 +203,10 @@ bool run(bool int8) {
    {
        const int64_t R = 1500, b1 = n_blocks, b0 = b1 - R;
        Pools ring;
-        ring.alloc(R, s, int8, false);
+        ring.alloc(R, s, fmt, false);
        int32_t* rt = dalloc<int32_t>(n_blocks);
        k::kv_ring_table(rt, n_blocks, R, nullptr);
-        k::kv_ring_restore(ring.attn(rt), host.p, int8, b0, b1, R, s, nullptr);
+        k::kv_ring_restore(ring.attn(rt), host.p, fmt, b0, b1, R, s, nullptr);
        const int64_t width = cap;
        std::vector<int32_t> hids((size_t) cap), hst = {(int32_t) (N - 1), (int32_t) N, (int32_t) (N / 4), (int32_t) width};
        for (int64_t i = 0; i < width; ++i) hids[i] = (int32_t) (N - width + i);   // the window's last cells
@@ -211,7 +218,7 @@ bool run(bool int8) {
        ck(cudaMemcpy(a.data(), out_ref, (size_t) NH * D * 4, cudaMemcpyDeviceToHost), "a");
        ck(cudaMemcpy(b2.data(), out_str, (size_t) NH * D * 4, cudaMemcpyDeviceToHost), "b");
        const bool ok = std::memcmp(a.data(), b2.data(), (size_t) NH * D * 4) == 0;
-        std::printf("  %s ring restore: %s\n", int8 ? "int8" : "fp16", ok ? "identical" : "DIFFERS");
+        std::printf("  %s ring restore: %s\n", name, ok ? "identical" : "DIFFERS");
        if (!ok) ++bad;
    }
    return bad == 0;
@@ -220,8 +227,8 @@ bool run(bool int8) {

 int main() {
    std::printf("kv_stream_parity: streamed vs resident KV, bitwise\n");
-    const bool a = run(true), b = run(false);
-    if (!a || !b) ++g_fail;
+    const bool a = run(k::kKvInt8), b = run(k::kKvF16), c = run(k::kKvQ4);
+    if (!a || !b || !c) ++g_fail;
    std::printf(g_fail ? "FAIL\n" : "PASS\n");
    return g_fail ? 1 : 0;
 }
@@ -10,6 +10,8 @@
 #include "strata/kernels/cpu/expert_layout.hpp"
 #include "strata/kernels/qsa.hpp"
 #include "strata/kernels/kv_stream.hpp"
+#include "strata/kernels/cvec.hpp"
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/core/layer.hpp"
 #include "strata/kernels/qsa_decode_attn.hpp"
 #include "strata/kernels/qsa_select.hpp"
@@ -133,7 +135,10 @@ void take_stage(Alloc& o_borrowed, const core::SessionState& ss, const strata::k
    own.owned = o_borrowed.owned;
    Alloc& o = stage_own() ? own : o_borrowed;
    const size_t rows = (size_t) q0.n_pages * s.n_head_kv * s.page_size;
-    if (q0.kv_int8) {
+    if (q0.kv_q4) {
+        st.k_q4 = o.take<uint8_t>(rows * strata::kernels::kv_q4_bytes_per_head((int) s.head_dim), ok);
+        st.v_q4 = o.take<uint8_t>(rows * strata::kernels::kv_q4_bytes_per_head((int) s.head_dim), ok);
+    } else if (q0.kv_int8) {
        st.k_q = o.take<int8_t>(rows * s.head_dim, ok);
        st.v_q = o.take<int8_t>(rows * s.head_dim, ok);
        st.k_scale = o.take<uint16_t>(rows * (s.head_dim / 64), ok);
@@ -146,6 +151,7 @@ void take_stage(Alloc& o_borrowed, const core::SessionState& ss, const strata::k
 strata::kernels::QsaAttnPools pools_of(const strata::kernels::KvHostPools& h, const int32_t* table) {
    strata::kernels::QsaAttnPools p;
    p.k_pool = h.k_pool; p.v_pool = h.v_pool; p.k_q = h.k_q; p.v_q = h.v_q; p.k_scale = h.k_scale; p.v_scale = h.v_scale;
+    p.k_q4 = h.k_q4; p.v_q4 = h.v_q4;
    p.page_table = table;
    return p;
 }
@@ -424,14 +430,23 @@ bool Prefill::run(const int64_t* tokens, int64_t n, int64_t pos0, std::string& e
                    // chunk's cells go to the host copy, the staging pool, and the VRAM slots of resident blocks
                    const bool staged = st.kv_mode == 1;
                    if (staged)
-                        strata::kernels::kv_stage_from_host(pools_of(m.stage, m.ident_table), st.host, st.kv_int8,
+                        strata::kernels::kv_stage_from_host(pools_of(m.stage, m.ident_table), st.host,
+                                                            core::qsa_kv_format(st),
                                                            (p0 + s.page_size - 1) / s.page_size, s, m.cs);
-                    kv_append(m.Kc, m.Vc, T, p0, st.page_table, s.page_size, st.kv_int8 ? nullptr : st.k_pool,
-                              st.kv_int8 ? nullptr : st.v_pool, st.k_q, st.v_q, st.k_scale, st.v_scale, m.cs,
-                              &st.host, staged ? &m.stage : nullptr);
+                    if (st.kv_q4) {   // Q4_0 KV (kv_q4.hpp): rotated K and V, the queries below too, the output back
+                        strata::kernels::fwht256_inplace_cuda(m.Kc, T * 2, m.cs);
+                        strata::kernels::fwht256_inplace_cuda(m.Vc, T * 2, m.cs);
+                        strata::kernels::kv_append_q4(st.k_q4, st.v_q4, st.page_table, p0, T, m.Kc, m.Vc, s, m.cs,
+                                                      &st.host, staged ? &m.stage : nullptr);
+                    } else {
+                        kv_append(m.Kc, m.Vc, T, p0, st.page_table, s.page_size, st.kv_int8 ? nullptr : st.k_pool,
+                                  st.kv_int8 ? nullptr : st.v_pool, st.k_q, st.v_q, st.k_scale, st.v_scale, m.cs,
+                                  &st.host, staged ? &m.stage : nullptr);
+                    }
                    split_q(m.Qf, m.q, T, m.cs);
                    rms_rows(m.q, (const float*) wqn->data, T * 24, 256, 256, EPS, m.cs);
                    rope(m.q, T, 24, 256, 6144, p0, (float) strata::kernels::qsa_freq_base(), m.cs);
+                    if (st.kv_q4) strata::kernels::fwht256_inplace_cuda(m.q, T * 24, m.cs);
                    rms_rows(m.q_idx, (const float*) wiqn->data, T * 4, 128, 128, EPS, m.cs);
                    rope(m.q_idx, T, 4, 128, 512, p0, (float) strata::kernels::qsa_freq_base(), m.cs);
                    // the indexer appends, token by token; then scores + selection for many queries at once:
@@ -526,6 +541,7 @@ bool Prefill::run(const int64_t* tokens, int64_t n, int64_t pos0, std::string& e
                                                               m.steps_dev + t0 * strata::kernels::kStepCount, m.cap, s,
                                                               m.attn_scratch, m.attn + t0 * ZV, nb, m.cs);
                    }
+                    if (st.kv_q4) strata::kernels::fwht256_inplace_cuda(m.attn, T * 24, m.cs);
                    gate_attn(m.attn, m.Qf, m.attn_h, T, m.cs);
                    if (!native_proj(m.gemm, wo, m.attn_h, m.bo, T, v.name("attn_output.weight"), err)) return false;
                    ++qsa_index;
@@ -636,6 +652,8 @@ bool Prefill::run(const int64_t* tokens, int64_t n, int64_t pos0, std::string& e
                }
                // ---- the hyper-connection write of this half
                gr_write(m.R, m.bo, m.inj, HC, T, m.cs);
+                if (half == 1 && strata::kernels::cvec().covers(l))   // --control-vector-scaled
+                    strata::kernels::cvec_apply(m.R, l, T, D, nullptr, 0, nullptr, 0, false, m.cs);
            }
        }
        stats_.tokens += T;
@@ -34,6 +34,7 @@
 #include "strata/kernels/native_qsa_indexer.hpp"
 #include "strata/kernels/native_rope.hpp"
 #include "strata/kernels/mrope.hpp"
+#include "strata/kernels/kv_q4.hpp"
 #include "strata/kernels/qsa.hpp"
 #include "strata/core/native_head.hpp"
 #include "strata/core/verify.hpp"
@@ -43,6 +44,11 @@
 #include "strata/program/logits_selection.hpp"
 #include "strata/spec/draft_policy.hpp"
 #include "strata/spec/suffix_drafter.hpp"
+#include "strata/kernels/cvec.hpp"
+#ifndef NOMINMAX
+#define NOMINMAX   // gguf_reader.hpp includes windows.h
+#endif
+#include "strata/artifact/gguf_reader.hpp"

 #include <cuda_runtime.h>

@@ -258,6 +264,13 @@ struct Options {
    int suffix_draft = 3;
    /// The MTP's own window cap (0 = --spec): with --spec 6 --mtp-max-t 4 the long windows come from suffix matches.
    int mtp_max_t = 0;
+    /// A control vector on the residual stream (strata/kernels/cvec.hpp), with llama.cpp's flags: the
+    /// `experimental-speed-projection` profile passes `--control-vector-scaled FILE:1.0 --control-vector-layer-range
+    /// 4 44 --cvec-mode project --cvec-dir per-layer`.  None by default; --serve switches a loaded one per request.
+    std::vector<std::pair<std::string, float>> cvec_files;
+    int cvec_first = -1, cvec_last = -1;   ///< llama.cpp's defaults: 1 .. the last layer
+    int cvec_mode = 1;                     ///< 0 = project, 1 = add (llama.cpp's default)
+    int cvec_single = -1;                  ///< --cvec-dir single:L (project mode): layer L's direction everywhere
 };

 void usage() {
@@ -277,6 +290,8 @@ void usage() {
                 "  --ple-sync-submit    A/B arm: submit table reads on the token thread (default: an I/O thread)\n"
                 "  --kv fp16|int8       KV storage (plan v0.3 P7): int8 codes + fp16 scale per 64 values, half the\n"
                 "                       VRAM; default fp16 until gate G-C accepts int8\n"
+                 "  --kv q4_0            4-bit K/V after a Hadamard rotation (PR #21): half of int8's memory,\n"
+                 "                       slightly lower precision (see bench/results/2026-09-27-kv-q4)\n"
                 "  --kv-resident N      KV streaming: keep N cells of each QSA layer in VRAM (min 20480) and the\n"
                 "                       whole K/V in pinned RAM; the freed VRAM goes to expert slots. 0 (default):\n"
                 "                       all of it in VRAM. A context of N cells or fewer is not streamed\n"
@@ -327,6 +342,11 @@ void usage() {
                 "  --suffix-draft N     prompt lookup: draft from an earlier repeat of the last N+ tokens of context\n"
                 "                       when it pays (default 3; 0 = MTP only)\n"
                 "  --mtp-max-t M        cap the MTP's windows at M tokens (0 = --spec; longer ones come from suffixes)\n"
+                 "  --control-vector-scaled FILE:SCALE[,...]  a control vector GGUF on the residual stream (llama.cpp's\n"
+                 "                       format; --control-vector FILE = scale 1).  --serve: requests switch it (cvec=0|1)\n"
+                 "  --control-vector-layer-range A B  the layers it follows (inclusive; default 1 .. the last)\n"
+                 "  --cvec-mode add|project  h += s v (default) or h -= s (h.v) v with v unit\n"
+                 "  --cvec-dir per-layer|single:L  each layer's own direction (default) or layer L's everywhere (project)\n"
                 "  --no-token-graph     A/B: two graphs per layer (the host launches each) instead of one per token\n"
                 "  --no-fused-gr        A/B: the six-kernel hyper-connection read and a separate write (native)\n"
                 "  --prefill CHUNK      batched prompt processing in chunks of CHUNK tokens (needs --native)\n"
@@ -555,6 +575,80 @@ bool checkpoint_restore(const ConvCheckpoint& c, strata::core::SessionState& ss,
    return cudaDeviceSynchronize() == cudaSuccess;
 }

+// --control-vector-scaled: llama.cpp's `common_control_vector_load` (every file's `direction.<l>` times its scale,
+// summed; layer 0 has none) and `llama_adapter_cvec::apply` with the projection-mode patch (project: the unit
+// direction and its norm as the scale), into the tables `cvec_upload` takes.  `summary` is what INFO reports.
+bool load_control_vectors(const Options& o, const strata::core::ModelGeometry& g, std::string& summary, std::string& err) {
+    const int64_t L = g.n_layers, N = g.n_embd;
+    std::vector<float> data((size_t) (L * N), 0.0f);
+    std::vector<bool> have((size_t) L, false);
+    for (const auto& [path, scale] : o.cvec_files) {
+        try {
+            strata::GgufFile f(path);
+            const strata::MetaValue* arch = f.get("general.architecture");
+            if (arch == nullptr || arch->s != "controlvector") {
+                err = path + ": not a control vector GGUF (general.architecture is not 'controlvector')";
+                return false;
+            }
+            const strata::MetaValue* hint = f.get("controlvector.model_hint");
+            if (hint != nullptr && hint->s != "qwen4exp")
+                std::fprintf(stderr, "strata generate: %s was made for '%s', not qwen4exp\n", path.c_str(), hint->s.c_str());
+            int found = 0;
+            for (const strata::TensorInfo& t : f.tensors()) {
+                if (t.name.rfind("direction.", 0) != 0) continue;
+                const long l = std::strtol(t.name.c_str() + 10, nullptr, 10);
+                if (l < 1 || l >= L) continue;   // layer 0 has no vector; past the model is ignored, as in llama.cpp
+                if (t.type != 0 || t.elements() != (uint64_t) N) {
+                    err = path + ": " + t.name + " must be " + std::to_string((long long) N) + " f32";
+                    return false;
+                }
+                const float* src = reinterpret_cast<const float*>(f.tensor_data(t));
+                for (int64_t j = 0; j < N; ++j) data[(size_t) (l * N + j)] += scale * src[j];
+                have[(size_t) l] = true;
+                ++found;
+            }
+            if (found == 0) { err = path + ": no direction.<layer> tensors"; return false; }
+        } catch (const std::exception& e) {
+            err = e.what();
+            return false;
+        }
+    }
+    const int first = o.cvec_first <= 0 ? 1 : o.cvec_first;
+    const int last = (o.cvec_last <= 0 || o.cvec_last >= L) ? (int) L - 1 : o.cvec_last;
+    const int single = o.cvec_mode == 0 ? o.cvec_single : -1;
+    if (single >= 0 && (single >= L || !have[(size_t) single])) {
+        err = "--cvec-dir single:" + std::to_string(single) + ": the vector has no direction for that layer";
+        return false;
+    }
+    std::vector<float> dir((size_t) (L * N), 0.0f), s((size_t) L, 0.0f);
+    int steered = 0;
+    for (int64_t l = first; l <= last; ++l) {
+        const int64_t src = single >= 0 ? single : l;
+        if (!have[(size_t) src]) continue;
+        const float* d = data.data() + (size_t) (src * N);
+        if (o.cvec_mode == 0) {
+            double nrm = 0.0;
+            for (int64_t j = 0; j < N; ++j) nrm += (double) d[j] * d[j];
+            nrm = std::sqrt(nrm);
+            if (nrm <= 0.0) continue;
+            s[(size_t) l] = (float) nrm;
+            for (int64_t j = 0; j < N; ++j) dir[(size_t) (l * N + j)] = (float) (d[j] / nrm);
+        } else {
+            s[(size_t) l] = 1.0f;
+            std::copy(d, d + N, dir.begin() + (size_t) (l * N));
+        }
+        ++steered;
+    }
+    if (steered == 0) { err = "the control vector has no direction in layers " + std::to_string(first) + ".." + std::to_string(last); return false; }
+    if (!strata::kernels::cvec_upload(dir, s, o.cvec_mode, first, last, N, g.hc, err)) return false;
+    summary = std::string(o.cvec_mode == 0 ? "project" : "add") + ":" + std::to_string(first) + "-" + std::to_string(last) +
+              (single >= 0 ? ":single" + std::to_string(single) : "");
+    // the line llama.cpp's patched build prints, so a log shows the same thing
+    std::fprintf(stderr, "strata generate: control vector mode = %s, dir = %s, layers %d..%d (%d steered)\n",
+                 o.cvec_mode == 0 ? "project" : "add", single >= 0 ? "single" : "per-layer", first, last, steered);
+    return true;
+}
+
 }  // namespace

 int main(int argc, char** argv) {
@@ -677,6 +771,38 @@ int main(int argc, char** argv) {
        else if (a == "--short-read") o.short_read = std::max(0LL, std::atoll(next("--short-read")));
        else if (a == "--suffix-draft") o.suffix_draft = std::max(0, std::atoi(next("--suffix-draft")));
        else if (a == "--mtp-max-t") o.mtp_max_t = std::max(0, std::atoi(next("--mtp-max-t")));
+        else if (a == "--control-vector") o.cvec_files.push_back({next("--control-vector"), 1.0f});
+        else if (a == "--control-vector-scaled") {
+            // FILE:SCALE, comma-separated; the LAST colon splits, so a Windows path (C:\...) keeps its drive
+            std::stringstream list(next("--control-vector-scaled"));
+            std::string item;
+            while (std::getline(list, item, ',')) {
+                const size_t colon = item.rfind(':');
+                char* end = nullptr;
+                const float sc = colon == std::string::npos ? 0.0f : std::strtof(item.c_str() + colon + 1, &end);
+                if (colon == std::string::npos || colon == 0 || end == item.c_str() + colon + 1 || *end != '\0') {
+                    std::fprintf(stderr, "--control-vector-scaled: expected FILE:SCALE, got '%s'\n", item.c_str());
+                    return 2;
+                }
+                o.cvec_files.push_back({item.substr(0, colon), sc});
+            }
+        }
+        else if (a == "--control-vector-layer-range") {
+            o.cvec_first = std::atoi(next("--control-vector-layer-range"));
+            o.cvec_last = std::atoi(next("--control-vector-layer-range"));
+        }
+        else if (a == "--cvec-mode") {
+            const std::string m = next("--cvec-mode");
+            if (m == "project") o.cvec_mode = 0;
+            else if (m == "add") o.cvec_mode = 1;
+            else { std::fprintf(stderr, "--cvec-mode: add or project, got '%s'\n", m.c_str()); return 2; }
+        }
+        else if (a == "--cvec-dir") {
+            const std::string d = next("--cvec-dir");
+            if (d == "per-layer") o.cvec_single = -1;
+            else if (d.rfind("single:", 0) == 0) o.cvec_single = std::atoi(d.c_str() + 7);
+            else { std::fprintf(stderr, "--cvec-dir: per-layer or single:L, got '%s'\n", d.c_str()); return 2; }
+        }
        else if (a == "--no-spec-split") o.spec_split = false;
        else if (a == "--eos-ids") {
            std::string e;
@@ -725,11 +851,13 @@ int main(int argc, char** argv) {
        std::fprintf(stderr, "strata generate: invalid --ple-io/--ple-row-cache/--ple-inflight/--ple-delay-us\n");
        return 2;
    }
-    if (o.kv != "fp16" && o.kv != "int8") {
-        std::fprintf(stderr, "strata generate: --kv must be fp16 or int8\n");
+    if (o.kv == "q4") o.kv = "q4_0";
+    if (o.kv != "fp16" && o.kv != "int8" && o.kv != "q4_0") {
+        std::fprintf(stderr, "strata generate: --kv must be fp16, int8 or q4_0\n");
        return 2;
    }
    strata::core::qsa_set_kv_int8(o.kv == "int8");
+    strata::core::qsa_set_kv_q4(o.kv == "q4_0");   // PR #21: 4-bit codes after a Hadamard rotation (kv_q4.hpp)
    if (o.kv_resident < 0) {
        std::fprintf(stderr, "strata generate: --kv-resident must be >= 0\n");
        return 2;
@@ -943,6 +1071,15 @@ int main(int argc, char** argv) {
    strata::kernels::ple_set_native_postops(o.native_ple_postops);
    const strata::core::ModelGeometry g;
    const int64_t K = 10;
+    // before session_init: every graph captured from here on has the vector's kernels where it applies
+    std::string cvec_summary = "0";
+    if (!o.cvec_files.empty()) {
+        std::string ce;
+        if (!load_control_vectors(o, g, cvec_summary, ce)) {
+            std::fprintf(stderr, "strata generate: control vector: %s\n", ce.c_str());
+            return 2;
+        }
+    }
    if (o.max_context < (int64_t) o.tokens.size() + o.max_new) {
        std::fprintf(stderr, "strata generate: --max-context %lld cannot hold %zu prompt + %lld new tokens\n",
                     (long long) o.max_context, o.tokens.size(), (long long) o.max_new);
@@ -1168,6 +1305,28 @@ int main(int argc, char** argv) {
        std::fprintf(stderr, "strata generate: profile %s: %zu ranked pairs, built for %lld slots\n",
                     o.expert_profile.c_str(), profile.size(), (long long) pslots);
    }
+    // THE HEAD BEFORE THE CACHE.  The expert cache takes what is free minus the reserve, so everything allocated
+    // after it comes out of the reserve.  The native head (~0.5 GB with IQ3_S) was loaded after it and ate most of
+    // the 700 MiB: 128K IQ3_S ended with 30 MiB free, the driver paged, and a request stalled for good at its first
+    // verify window.  Loaded first, the cache is sized around it.
+    const strata::core::WeightRef* wo = wt.find("output.weight");
+    if (wo == nullptr) { std::fprintf(stderr, "strata generate: output.weight is missing\n"); return 1; }
+    const int64_t n_vocab = wo->ne1;
+    strata::core::NativeHead native_head;
+    if (!o.native_head_gguf.empty()) {
+        if (!native_head.load(o.native_head_gguf, g.n_embd, n_vocab, err)) {
+            std::fprintf(stderr, "strata generate: %s\n", err.c_str());
+            return 1;
+        }
+        std::fprintf(stderr, "strata generate: experimental native Q5_K head, %llu bytes\n",
+                     (unsigned long long) native_head.weight_bytes());
+    }
+    std::vector<float> logits((size_t) n_vocab);
+    float* d_logits = nullptr;
+    if (cudaMalloc(&d_logits, (size_t) n_vocab * 4) != cudaSuccess) {
+        std::fprintf(stderr, "strata generate: the logits buffer failed\n");
+        return 1;
+    }
    const bool auto_cache = o.expert_cache < 0;
    if (o.expert_cache < 0) {
        size_t free_b = 0, total_b = 0;
@@ -1250,19 +1409,16 @@ int main(int argc, char** argv) {
                     (long long) xcache.slots(), xcache.gib());
        mem_mark("opening the expert cache");
        xcache.set_per_layer_admission(o.expert_cache_per_layer);
-        // **ROUND 328: THE HIT PATH IS PROVABLY WRONG, AND THIS SAYS SO OUT LOUD RATHER THAN LETTING IT
-        // CORRUPT A RUN QUIETLY.**  With the cache on, the generated tokens DIVERGE from the cache-off run:
-        // at 256 global slots (2.97% hits) the first difference is at **token 40**; at 4096 per-layer slots
-        // (54.4% hits) it is at **token 0**.  The cache-off run is deterministic across repeated runs, so
-        // this is a real fault in `moe_hit_grouped_s2`'s inputs or the fill - not noise.  It also explains
-        // what R4 recorded as "a better profile makes the token worse": more hits means more wrong rows, so
-        // the payoff is non-monotone BY CONSTRUCTION rather than by any memory-system effect.
-        // The cache stays opt-in and this warning is not a refusal, because the divergence IS the diagnostic.
+        // Round 328 warned here that the GPU hit path was wrong (tokens diverged from a cache-off run from
+        // token 0). That fault was fixed long since (native_expert_parity, expert_parity, the grouped kernels'
+        // tests), and the warning outlived it (issue #23). What remains is rounding: a GPU expert and the CPU's
+        // compute the same quantized expert with different float order, so a near-tie can flip. Measured teacher-
+        // forced on 2,557 tokens (bench/results/2026-09-27-cache-parity): 95-98% same top-1, and perplexity equal
+        // (on - off = -0.005 +- 0.005 nats). Neither output is more correct than the other.
        std::fprintf(stderr,
-                     "strata generate: *** WARNING: --expert-cache is enabled and the GPU hit path is NOT\n"
-                     "                 CORRECT. The generated tokens diverge from a cache-off run (measured:\n"
-                     "                 first difference at token 40 at 2.97%% hits, token 0 at 54.4%%). Any\n"
-                     "                 timing from this run is real; any OUTPUT from it is not. ***\n");
+                     "strata generate: the GPU computes the experts in the cache; it rounds differently from the CPU,\n"
+                     "                 so a reply can differ slightly from a run without the cache (same quality:\n"
+                     "                 bench/results/2026-09-27-cache-parity).\n");
        if (o.expert_cache_per_layer) {
            int64_t lo = 0, hi = 0;
            xcache.layer_slot_range(0, lo, hi);
@@ -1479,25 +1635,7 @@ int main(int argc, char** argv) {
    }

    mem_mark("the expert cache and the graphs");
-    std::fprintf(stderr, "strata generate: session is up; locating the head\n");
-    const strata::core::WeightRef* wo = wt.find("output.weight");
-    if (wo == nullptr) { std::fprintf(stderr, "strata generate: output.weight is missing\n"); return 1; }
-    const int64_t n_vocab = wo->ne1;
-    strata::core::NativeHead native_head;
-    if (!o.native_head_gguf.empty()) {
-        if (!native_head.load(o.native_head_gguf, g.n_embd, n_vocab, err)) {
-            std::fprintf(stderr, "strata generate: %s\n", err.c_str());
-            return 1;
-        }
-        std::fprintf(stderr, "strata generate: experimental native Q5_K head, %llu bytes\n",
-                     (unsigned long long) native_head.weight_bytes());
-    }
-    std::vector<float> logits((size_t) n_vocab);
-    float* d_logits = nullptr;
-    if (cudaMalloc(&d_logits, (size_t) n_vocab * 4) != cudaSuccess) {
-        std::fprintf(stderr, "strata generate: the logits buffer failed\n");
-        return 1;
-    }
+    std::fprintf(stderr, "strata generate: session is up\n");
    auto run_head = [&](void* stream) -> bool {
        if (!native_head.loaded())
            return strata::core::lm_head(wt, g, ss.block, d_logits, stream, err);
@@ -2033,6 +2171,7 @@ int main(int argc, char** argv) {
        std::vector<ImgKey> live_imgs, req_imgs;
        bool live_ok = false;
        std::vector<ConvCheckpoint> checks;
+        bool cvec_cached = true;   // the control vector's state the live session and the checkpoints were read with
        int64_t pp_total = 0, pp_from = 0, pp_next_check = 0;
        Clock::time_point pp_t0 = Clock::now();
        auto imgs_below = [&](const std::vector<ImgKey>& all, int64_t L) {
@@ -2172,9 +2311,29 @@ int main(int argc, char** argv) {
            // and a page-in while the verify graph spins on a host flag stalls the request for good
            size_t free_b = 0, total_b = 0;
            cudaMemGetInfo(&free_b, &total_b);
-            std::fprintf(stderr, "strata serve: %lld MiB of VRAM free with everything loaded%s\n",
-                         (long long) (free_b >> 20), free_b < ((size_t) 128 << 20)
-                             ? " - LOW: requests may stall; lower --max-context or raise --vram-reserve-mib" : "");
+            // below ~256 MiB a later allocation (a first-used window's buffers, the desktop, another program) can make
+            // the driver page GPU memory, and a verify graph spinning on a host flag then never finishes
+            const int64_t free_mib = (int64_t) (free_b >> 20);
+            if (free_mib >= 256) {
+                std::fprintf(stderr, "strata serve: %lld MiB of VRAM free with everything loaded\n", (long long) free_mib);
+            } else {
+                std::fprintf(stderr, "strata serve: %lld MiB of VRAM free with everything loaded - LOW: requests may stall;"
+                                     " add --vram-reserve-mib %lld to the config's args (or lower --max-context)\n",
+                             (long long) free_mib, (long long) (o.vram_reserve_mib + 512 - free_mib));
+            }
+        }
+        // what the server's Monitor tab shows (servers before 0.1.8 skip unknown lines until READY)
+        {
+            size_t free_b = 0, total_b = 0;
+            cudaMemGetInfo(&free_b, &total_b);
+            std::printf("INFO context=%lld kv=%s kv_resident=%lld expert_slots=%lld expert_cache_mib=%lld spec=%d "
+                        "mtp_max=%d lookup=%d vram_free_mib=%lld cvec=%s arena_mib=%lld\n",
+                        (long long) o.max_context, o.kv.c_str(),
+                        (long long) (g.n_qsa_layers() > 0 && ss.qsa_states[0].kv_mode == 1
+                                         ? ss.qsa_states[0].n_slots * 4 : 0),
+                        (long long) xcache.slots(), (long long) (xcache.bytes() >> 20), o.spec, o.mtp_max_t,
+                        o.suffix_draft, (long long) (free_b >> 20), cvec_summary.c_str(),
+                        (long long) (strata::kernels::cpu::expert_layout().total >> 20));
        }
        std::printf("READY %lld stop\n", (long long) o.max_context);   // "stop": this engine honours STOP
        std::fflush(stdout);
@@ -2211,7 +2370,8 @@ int main(int argc, char** argv) {
            unsigned long long req_seed = 0;
            float req_min_p = 0.0f, req_penalty_repeat = 1.0f, req_penalty_freq = 0.0f, req_penalty_present = 0.0f;
            int req_penalty_last_n = 0;
-            if (!geni && endp != nullptr) {
+            int req_cvec = 1;   // cvec=0|1: a loaded control vector for this request (on when absent)
+            if (endp != nullptr) {   // GENI takes only cvec=; its file path is the first token without an =
                for (;;) {
                    while (*endp == ' ') ++endp;
                    const char* start = endp;
@@ -2222,7 +2382,9 @@ int main(int argc, char** argv) {
                    if (eq == std::string::npos) { endp = const_cast<char*>(start); break; }
                    const std::string key = tok.substr(0, eq);
                    const float fv = std::strtof(tok.c_str() + eq + 1, nullptr);
-                    if (key == "temperature") req_temperature = fv;
+                    if (key == "cvec") req_cvec = std::atoi(tok.c_str() + eq + 1);
+                    else if (geni) {}   // image requests decode greedily
+                    else if (key == "temperature") req_temperature = fv;
                    else if (key == "top_p") req_top_p = fv;
                    else if (key == "top_k") req_top_k = std::atoi(tok.c_str() + eq + 1);
                    else if (key == "min_p") req_min_p = fv;
@@ -2348,6 +2510,17 @@ int main(int argc, char** argv) {
                    if ((int32_t) ids[(size_t) i] != pre[(size_t) i]) return false;
                return imgs_below(req_imgs, L) == pre_imgs;
            };
+            // the control vector for this request.  The live session and the checkpoints were read one way, so a
+            // switch reads the prompt again from the start
+            if (strata::kernels::cvec().loaded()) {
+                const bool want = req_cvec != 0;
+                if (want != cvec_cached) {
+                    live_ok = false;
+                    checks.clear();
+                    cvec_cached = want;
+                }
+                strata::kernels::cvec_set_enabled(want);
+            }
            int64_t resume = 0;
            bool from_live = false;
            if (o.prompt_cache > 0) {
@@ -2697,6 +2870,19 @@ int main(int argc, char** argv) {
                uint64_t h_ple = hash_dev(ss.ple_hist, z.ple, 1469598103934665603ull);
                uint64_t h_tail = 1469598103934665603ull, h_pool = h_tail, h_kv = h_tail, h_stale = h_tail;
                const int64_t kvb = qs.head_dim, scb = (qs.head_dim / 64) * 2;
+                // a state's K/V arrays and their bytes per (cell, head) row: the host copy when it has one
+                auto kv_arrays = [&](const strata::core::QsaState& st) {
+                    const bool h = st.kv_mode != 0;
+                    std::vector<std::pair<const void*, int64_t>> a;
+                    if (st.kv_q4) {
+                        const int64_t q4b = (int64_t) strata::kernels::kv_q4_bytes_per_head((int) qs.head_dim);
+                        a = {{h ? st.host.k_q4 : st.k_q4, q4b}, {h ? st.host.v_q4 : st.v_q4, q4b}};
+                    } else {
+                        a = {{h ? st.host.k_q : st.k_q, kvb}, {h ? st.host.v_q : st.v_q, kvb},
+                             {h ? st.host.k_scale : st.k_scale, scb}, {h ? st.host.v_scale : st.v_scale, scb}};
+                    }
+                    return a;
+                };
                const int64_t end_cell = std::min<int64_t>(((L + qs.page_size - 1) / qs.page_size) * qs.page_size,
                                                           ss.qsa_states[0].max_cells);
                for (int64_t i = 0; i < g.n_qsa_layers(); ++i) {
@@ -2704,11 +2890,7 @@ int main(int argc, char** argv) {
                    h_tail = hash_dev(st.idx_tail, z.tail, h_tail);
                    h_pool = hash_dev(st.idx_pooled, (size_t) (L / qs.idx_block) * qs.idx_dim * 4, h_pool);
                    // KV streaming: the host copy is the identity layout and holds every cell
-                    const bool hs = st.kv_mode != 0;
-                    for (const auto& [pool, w] : {std::pair<const void*, int64_t>{hs ? st.host.k_q : st.k_q, kvb},
-                                                  {hs ? st.host.v_q : st.v_q, kvb},
-                                                  {hs ? st.host.k_scale : st.k_scale, scb},
-                                                  {hs ? st.host.v_scale : st.v_scale, scb}}) {
+                    for (const auto& [pool, w] : kv_arrays(st)) {
                        h_kv = hash_cells(pool, w, 0, L, h_kv);
                        h_stale = hash_cells(pool, w, L, end_cell, h_stale);
                    }
@@ -2716,11 +2898,7 @@ int main(int argc, char** argv) {
                const strata::core::QsaState& ms = mtp.kv_state();
                uint64_t h_mtp = 1469598103934665603ull;
                const int64_t mL = std::min<int64_t>(L, ms.max_cells);
-                const bool mh = ms.kv_mode != 0;
-                for (const auto& [pool, w] : {std::pair<const void*, int64_t>{mh ? ms.host.k_q : ms.k_q, kvb},
-                                              {mh ? ms.host.v_q : ms.v_q, kvb},
-                                              {mh ? ms.host.k_scale : ms.k_scale, scb},
-                                              {mh ? ms.host.v_scale : ms.v_scale, scb}})
+                for (const auto& [pool, w] : kv_arrays(ms))
                    if (pool != nullptr) h_mtp = hash_cells(pool, w, 0, mL, h_mtp);
                std::fprintf(stderr, "strata serve: STATE_HASH L=%lld gdn=%016llx ple=%016llx tail=%016llx pooled=%016llx "
                                     "kv=%016llx mtp=%016llx stale=%016llx ple_prev=%d,%d\n", (long long) L,
Author	SHA1	Message	Date
Niko1221	1e3bded2f7	web app: "Use for other apps too" - the Chat settings as every client's defaults A switch in the Sampling drawer. With it on, Apply stores the drawer's settings on the server (POST /settings): thinking level, temperature, top-p, top-k, seed, max tokens and the experimental speed projection. omp and other API clients then get them for anything they don't send themselves, from their next request (no restart); what a client sends still wins. Off again, clients use their own settings. - Kept between starts in <config>.shared-settings.json next to the run config; the server window says what other apps use. - Only Strata's own page can change them: JSON only (a cross-site form or simple request can't send it without a CORS preflight, which this server never grants), no foreign Origin, and the API key when one is set. - Values are checked (bad ones are a 400 naming the setting). - Tests: defaults reach the engine, a client's own values win, off clears them and the file, 415/403/400/401.	2026-09-27 17:50:38 +02:00
Niko1221	ae3dc218ea	web app: a long switch label no longer squeezes the switch The Sampling drawer's "Experimental speed projection" row has a longer label than "Show thinking", and the flex row shrank its switch, clipping the knob at the drawer's edge. Switches in toggle rows keep their size.	2026-09-27 17:45:18 +02:00
Niko	3cba59ea31	Update README.md	2026-09-27 17:48:41 +02:00
Niko	ecba7dfc10	Update README.md	2026-09-27 17:46:12 +02:00
Niko1221	1314ebfc7f	README: a video up top, speed and model choice first, simpler troubleshooting (engine 0.1.10) - An animated preview of the pagoda garden demo under the title (the full video is a release download: GitHub does not play videos from a repository), and a screenshot of the app in "Using it". - "Is my PC enough?" folded into Install; "How fast is it?" and "Which model should I pick?" come first. The model table shows shard 1 (what gets loaded) instead of the download, with a plain fit rule: RAM >= shard 1 + ~10 GB, since the experts live in RAM. - "Something went wrong?" as short questions and answers, including the first-start freeze and "the engine stopped unexpectedly". - DETAILS troubleshooting: the same two rows, and the VRAM stall row now points to engine 0.1.9. - setup requires engine 0.1.10 (INFO arena_mib, the start-up RAM warning).	2026-09-27 17:30:09 +02:00
Niko1221	7664149081	serve: an engine that dies mid-answer is an error, and the next request restarts it (issue #27 ) On Linux with too little RAM for the model's experts (IQ3_XXS on 47 GB), the out-of-memory killer ended the engine mid-answer. The server then recorded the reply as "length" ("Max tokens" in the Monitor, no tok/s), the stream was cut with a traceback, and every later request failed on the dead pipe. - The engine's death is EngineDied: the reply ends with a clear error event (OpenAI and Anthropic streams; 503 before the answer), the Monitor says Error, and the server window names the likely cause (RAM; dmesg). - The next request starts the engine again (same command) instead of failing forever; the engine counts as gone as soon as its output closes, and a failed write to it is not mistaken for the client leaving. - The engine's INFO line reports its expert arena (arena_mib); at start the server warns when less than ~6 GB of RAM is left beside it. - Test: a fake engine dies mid-stream -> error event + [DONE], Monitor "error", the next request restarts it and answers. End to end with Q2_0: strata.exe killed mid-answer -> error (exit code 1); next request restarts it (31 s) and answers.	2026-09-27 17:23:55 +02:00
Niko1221	6abee7f397	Experimental speed projection: an optional control vector (EXPERIMENTAL, off by default) The engine takes llama.cpp's control-vector flags (--control-vector-scaled FILE:SCALE, --control-vector-layer-range A B, --cvec-mode add\|project, --cvec-dir per-layer\|single:L) and applies the vector after each steered layer's FFN write on every hyper-connection stream: project h -= s (h.v) v, or add h += d - where the experimental-speed-projection package's patched llama.cpp applies it (build_cvec after the layer's second combine). - strata/kernels/cvec: one kernel per (stream, token); with a pending fused write it applies it first with the fused read's own arithmetic, so a loaded-but-off vector is bitwise the stock engine. cvec_parity tests it. - Per request: cvec=0\|1 on the GEN/GENI line, the API field "experimental_speed_projection", a switch in the web app's Sampling drawer; a switch drops the conversation cache once. About and the Monitor say which way each request ran. - setup asks (default off; --experimental-speed-projection on\|off\|GGUF), original Qwen3.8-Flash-Next only. The vector ships in data/experimental-speed-projection with a README that says what it is: its package describes it as a refusal-direction projection. Measured (bench/results/2026-09-27-esp): off = stock token for token; on costs 0.2-0.4% per token; mean KL 0.063 on 2,557 teacher-forced tokens.	2026-09-27 16:47:45 +02:00
Niko1221	868dc724d6	serve: reachable from other devices on the network (issue #26 ) --host 0.0.0.0 bound fine, but nothing in the normal flow could set it: the generated run scripts and setup's start always used the default 127.0.0.1, the config had no host, and the server then printed http://0.0.0.0:8080, which opens nothing on another device. - "host" in strata-<model>.json (the run scripts need no change); setup takes --host and --api-key and writes them there. - With a non-local host the server prints this PC's addresses (the default route's first), warns when there is no API key, and on Windows says how to let it through the firewall. The local lines name 127.0.0.1. - Documented in DETAILS.	2026-09-27 16:42:27 +02:00
Niko1221	f1b35531c3	engine: size the expert cache around the output head, so VRAM stays free (engine 0.1.9) The expert cache takes what is free minus --vram-reserve-mib (700), so everything allocated after it comes out of that reserve. The native output head (0.44 GB Q2_0, 0.52 GB IQ3_S) was loaded after it and ate most of it: IQ3_S at 128K ended with 30 MiB free, the driver paged GPU memory, and a request stalled for good at its first verify window (GPU "100%" at 45 W). Q2_0 at 32K ended with 75 MiB. The head now loads before the cache is sized. Same reserve, now actually free: 480-626 MiB with everything loaded (Q2_0 32K, IQ3_S 128K + vision). The cache is ~8% smaller (Q2_0 32K: 3,848 slots vs 4,167) and decode is not slower: 78-79 tok/s median vs 71.7 for 0.1.8 on the same greedy prompts. Below 256 MiB the startup line now says which --vram-reserve-mib to add.	2026-09-27 16:23:07 +02:00
Niko1221	20351c6030	serve: --fit-max-tokens follow-ups (PR #24 ) - A prompt that leaves no room at all is still the clear 400 with the flag on (it clamped to 1 token, which the engine then refused as over the context). - "fit_max_tokens": true in the run config switches it on too, since the run scripts are generated by setup. - Tests: an overshoot is clamped to the room on both APIs, a budget that fits is unchanged, no room is a 400. Documented in DETAILS.	2026-09-27 16:12:38 +02:00
Niko1221	bbba5bf576	Merge PR #24 : optional --fit-max-tokens (clamp the output cap to the context instead of 400)	2026-09-27 16:11:54 +02:00
pete	5f563ce3a3	serve: optional --fit-max-tokens clamps the output cap to remaining context instead of rejecting Agentic clients legitimately request the model's full output cap with a long prompt (e.g. prompt 131,888 + max_tokens 130,298 on a 262,144 window: rejected over a 42-token overshoot). Default behaviour is unchanged: without the flag the server still rejects with 400, like llama.cpp. With --fit-max-tokens the server clamps max_tokens to the remaining room (max_context - CTX_SLACK - prompt, floor 1 token) and answers. Measured on a 262,144-token host: prompt 251,455 + max_tokens 131,072 -> HTTP 200, completion_tokens 10,681 (= 262,144 - 251,455 - 8), finish length. Flag off: byte-identical 400. Small requests unaffected in both modes.	2026-09-27 13:11:09 +01:00
Niko1221	6c431c60cc	Record PR #21 's rebased head as merged (its change is already in via `7f21e2b`)	2026-09-27 12:14:51 +02:00
Niko1221	822a5c783f	Web app: ship the Outfit font (OFL); the theme follows the system until picked; engine 0.1.8 - serve/web/fonts: Outfit as one variable font (latin + latin-ext, weights 100-900) with its OFL.txt, in place of the design kit's commercial font. - The page no longer saves the system's theme on first load (which froze it); only a click on the theme button or toggle is remembered, and the page follows system changes until then. - setup: requires engine 0.1.8 (--kv q4_0, the INFO line).	2026-09-27 12:13:45 +02:00
Niko1221	0d721cf87b	Web app: Chat, Monitor and About tabs (#22 ) The browser page at http://127.0.0.1:8080 is now the Strata app, in the Strata design language (serve/web, same origin, no CDN): - Chat: streaming answers, collapsible thinking, Markdown with code copy, images when enabled, sampling/thinking settings, export, new chat. - Monitor: live model state (reading with progress / generating at tok/s), GPU load, VRAM, temperature, power, PCIe traffic, CPU, RAM, disk, the context fill and the last requests, each with a minute of history. - About: the model and engine settings, this PC, the API addresses. Server: GET /metrics (API key when set), /web/* app files, /fonts/* only when installed locally (not shipped; the page falls back to the system font). serve/telemetry.py reads NVML via ctypes and psutil (new setup dependency) with fallbacks. The engine prints an INFO line before READY (kv, expert slots, speculation); older servers ignore it and the server works with older engines. Based on the dashboard idea in PR #22 by code-martin.	2026-09-27 12:00:33 +02:00
Niko1221andcode-martin	40542e8bb3	Merge PR #21 : Q4_0 KV with Hadamard rotation (--kv q4_0), integrated with KV streaming; accurate expert-cache note (#23 ) PR #21 (code-martin): a 4-bit K/V format (orthonormal FWHT-256 rotation of K, V and Q, then q4_0 blocks). On merge: - Q4 is a full third format of the KV-streaming code (host copy, block residency, prompt-path staging, MTP ring, STATE_HASH): with --kv-resident the PR wrote K/V through the residency map's -1 entries. kv_stream_parity now checks Q4 streamed vs resident, bitwise. - The per-token path ran attention twice (once before the blocks were resident); once now. - The q4 group maximum breaks \|x\| ties the same way in every lane (one scale per block). - Dropped: the duplicate path fixes (in 0.1.7), personal .gitignore entries, the re-encoded CMakeLists. - Measured (bench/results/2026-09-27-kv-q4): int8 matches fp16; q4_0 costs precision on long documents (perplexity +8-12%), needles 5/5, +3.7% decode at 128K. Opt-in: setup asks above 8K context (int8 recommended), START-HERE.bat --kv q4_0, or --kv q4_0 in the config. #23: the start-up warning "the GPU hit path is NOT CORRECT" dated from a long-fixed fault. Measured teacher-forced on 2,557 tokens (bench/results/2026-09-27-cache-parity): cache on vs off, 95-98% same top-1 and equal perplexity (-0.005 +- 0.005 nats); the warning is now one accurate line. Co-Authored-By: code-martin <[email protected]>	2026-09-27 11:36:28 +02:00
code-martin	e8b75ff2f6	feat(kv): add Q4_0 KV cache mode with FWHT-256 Walsh-Hadamard rotation - Implement Q4_0 KV quantization and dequantization kernels with scale/offset - Implement fast Walsh-Hadamard transform (FWHT-256) rotation for outlier suppression - Add Q4_0 attention decode support in qsa_decode_attn - Add kv_q4_parity parity test verifying CPU vs CUDA numerical accuracy - Support --kv q4_0 in generate CLI and runtime layer allocation - Update .gitignore for build and repository hygiene	2026-09-27 11:48:16 +03:00
code-martin	7f21e2b401	feat(kv): add Q4_0 KV cache mode with FWHT-256 Walsh-Hadamard rotation - Implement Q4_0 KV quantization and dequantization kernels with scale/offset - Implement fast Walsh-Hadamard transform (FWHT-256) rotation for outlier suppression - Add Q4_0 attention decode support in qsa_decode_attn - Add kv_q4_parity parity test verifying CPU vs CUDA numerical accuracy - Support --kv q4_0 in generate CLI and runtime layer allocation - Update .gitignore for build and repository hygiene	2026-09-27 11:29:08 +03:00