mirror of
https://github.com/modelscope/FunASR.git
synced 2026-10-12 02:19:18 +00:00
Merge pull request #3573 from modelscope/codex/funasr-1.4.8-moss-vllm
chore(release): prepare 1.4.8
This commit is contained in:
zhifu gao
@@ -333,6 +333,7 @@ inference on physical Blackwell hardware.
|
||||
|
||||
## What's new
|
||||
|
||||
- 2026/08/30: **v1.4.8 on PyPI** — completes the third-party MOSS-Transcribe-Diarize vLLM path added after v1.4.7. `backend="vllm"` now accepts the official `response_format=diarized_json` speaker segments and normalizes them into FunASR `sentence_info`; `max_completion_tokens` is forwarded for long meetings, with 8192 tokens documented for recordings that exceed the default 5120-token boundary. MOSS still performs transcription, timestamps, and diarization end to end, so do not add external `vad_model` or `spk_model`. Upgrade with `python -m pip install -U "funasr==1.4.8"`. The GitHub Release pairs this package with the verified ten-platform llama.cpp v0.2.6 archives. [MOSS deployment guide ->](./docs/moss_transcribe_diarize.md) · [Release ->](https://github.com/modelscope/FunASR/releases/tag/v1.4.8)
|
||||
- 2026/08/30: **llama.cpp runtime v0.2.6** — adds a dedicated Windows CUDA architecture 120 (`sm_120`) archive for RTX 50 / Blackwell GPUs while retaining the architecture 86 package. The two CUDA ZIPs bundle the required NVIDIA cuBLAS DLLs and license, statically link the MSVC runtime, and pass PE import audits; all ten Linux, macOS, and Windows archives are built from one exact release commit and checked against their published SHA-256 values. Build and package verification does not prove successful inference on physical Blackwell hardware, so hardware reports remain open until users retest the matching archive. [Implementation ->](https://github.com/modelscope/FunASR/pull/3570) · [Release ->](https://github.com/modelscope/FunASR/releases/tag/runtime-llamacpp-v0.2.6)
|
||||
- 2026/08/30: **v1.4.7 on PyPI** — integrates the third-party OpenMOSS MOSS-Transcribe-Diarize model with FunASR `AutoModel`. Choose local Transformers (`backend="hf"`) or an existing vLLM server (`backend="vllm"`); both paths normalize the tagged model output into `text`, `raw_text`, millisecond `timestamp`, and speaker-aware `sentence_info`. MOSS performs transcription, timestamps, and diarization in one pass, so omit external `vad_model` and `spk_model`. This release also improves SRT cue continuity across tiny same-speaker gaps and adds opt-in realtime decode profiling for diagnosing long-session latency. Upgrade with `python -m pip install -U "funasr==1.4.7"`. The GitHub Release carries the verified nine-platform llama.cpp v0.2.5 runtime archives. [MOSS deployment guide ->](./docs/moss_transcribe_diarize.md) · [Release ->](https://github.com/modelscope/FunASR/releases/tag/v1.4.7)
|
||||
- 2026/08/29: **llama.cpp runtime v0.2.5** — uploads host weights into the selected Vulkan backend buffer before graph execution. Q8 and F16 weights pass local Linux Vulkan llvmpipe validation, and all nine Linux, macOS, and Windows archives were built from exact commit `f371370d4c5e4c61d13d4eb9c55cda2f4dd95e4f` and verified against their published SHA-256 values. This does not claim the AMD Windows hardware crash is fixed; [#3479](https://github.com/modelscope/FunASR/issues/3479) remains open for reporter retesting. [Fix ->](https://github.com/modelscope/FunASR/pull/3555) · [Release ->](https://github.com/modelscope/FunASR/releases/tag/runtime-llamacpp-v0.2.5)
|
||||
|
||||
@@ -99,6 +99,7 @@ Whisper は単一モデルですが、**FunASR はツールキット**です—
|
||||
|
||||
## 最新情報
|
||||
|
||||
- 2026/08/30：**v1.4.8 を PyPI に公開** — v1.4.7 以降に merge された third-party MOSS-Transcribe-Diarize の vLLM path を完成させました。`backend="vllm"` は公式の `response_format=diarized_json` speaker segment を受け取り、FunASR `sentence_info` に正規化します。長時間会議では `max_completion_tokens` を server に渡せ、default の 5120-token 境界を超える録音向けに 8192 tokens を案内します。MOSS は transcription、timestamp、speaker diarization を end-to-end で実行するため、外部の `vad_model` や `spk_model` は追加しません。更新：`python -m pip install -U "funasr==1.4.8"`。GitHub Release は検証済み llama.cpp v0.2.6 の 10 platform archive と組み合わせます。[MOSS guide ->](./docs/moss_transcribe_diarize.md) · [Release ->](https://github.com/modelscope/FunASR/releases/tag/v1.4.8)
|
||||
- 2026/08/30：**llama.cpp runtime v0.2.6** — RTX 50 / Blackwell 向けに Windows CUDA architecture 120（`sm_120`）専用 archive を追加し、architecture 86 package も維持します。2 つの CUDA ZIP は必要な NVIDIA cuBLAS DLL と license を同梱し、MSVC runtime を static link して PE import audit に合格しています。Linux、macOS、Windows 向け 10 archive は同一の exact release commit から build し、公開 SHA-256 と照合します。Build と package の検証は Blackwell 実機 inference の成功を証明しないため、hardware report は対応 archive の再テストまで open のままです。[実装 →](https://github.com/modelscope/FunASR/pull/3570) · [Release →](https://github.com/modelscope/FunASR/releases/tag/runtime-llamacpp-v0.2.6)
|
||||
- 2026/08/30：**v1.4.7 を PyPI に公開** — OpenMOSS の third-party model MOSS-Transcribe-Diarize を FunASR `AutoModel` に統合しました。local Transformers（`backend="hf"`）または既存の vLLM server（`backend="vllm"`）を選択でき、どちらも tag 付き出力を `text`、`raw_text`、millisecond `timestamp`、speaker-aware `sentence_info` に正規化します。MOSS は transcription、timestamp、speaker diarization を 1 回の inference で実行するため、外部の `vad_model` と `spk_model` は指定しません。さらに、同一 speaker のごく短い gap をまたぐ SRT cue の連続性を改善し、long session latency の診断用に opt-in realtime decode profiling を追加しました。更新：`python -m pip install -U "funasr==1.4.7"`。GitHub Release には検証済みの llama.cpp v0.2.5 9 platform runtime archive も含まれます。[MOSS guide →](./docs/moss_transcribe_diarize.md) · [Release →](https://github.com/modelscope/FunASR/releases/tag/v1.4.7)
|
||||
- 2026/08/29：**llama.cpp runtime v0.2.5** — graph 実行前に host weight を選択された Vulkan backend buffer へ upload します。Q8/F16 weight は Linux Vulkan llvmpipe で検証済みで、Linux、macOS、Windows 向け 9 archive は exact commit `f371370d4c5e4c61d13d4eb9c55cda2f4dd95e4f` から build し、公開 SHA-256 と照合しました。AMD Windows hardware crash の修正済みとはしておらず、[#3479](https://github.com/modelscope/FunASR/issues/3479) は reporter の実機再テスト待ちで open のままです。[修正 →](https://github.com/modelscope/FunASR/pull/3555) · [Release →](https://github.com/modelscope/FunASR/releases/tag/runtime-llamacpp-v0.2.5)
|
||||
|
||||
@@ -99,6 +99,7 @@ Whisper는 단일 모델이지만, **FunASR는 툴킷**입니다. 용도에 맞
|
||||
|
||||
## 최신 소식
|
||||
|
||||
- 2026/08/30: **v1.4.8 PyPI 공개** — v1.4.7 이후 merge된 third-party MOSS-Transcribe-Diarize vLLM path를 완성했습니다. `backend="vllm"`은 공식 `response_format=diarized_json` speaker segment를 받아 FunASR `sentence_info`로 정규화합니다. 긴 회의에서는 `max_completion_tokens`를 server에 전달하며, default 5120-token 경계를 넘는 녹음에는 8192 tokens를 안내합니다. MOSS는 transcription, timestamp, speaker diarization을 end-to-end로 수행하므로 외부 `vad_model`이나 `spk_model`을 추가하지 않습니다. 업데이트: `python -m pip install -U "funasr==1.4.8"`. GitHub Release는 검증된 llama.cpp v0.2.6 10개 platform archive와 함께 제공합니다. [MOSS guide ->](./docs/moss_transcribe_diarize.md) · [Release ->](https://github.com/modelscope/FunASR/releases/tag/v1.4.8)
|
||||
- 2026/08/30: **llama.cpp runtime v0.2.6** — RTX 50 / Blackwell용 Windows CUDA architecture 120(`sm_120`) 전용 archive를 추가하면서 architecture 86 package도 유지합니다. 두 CUDA ZIP은 필요한 NVIDIA cuBLAS DLL과 license를 포함하고 MSVC runtime을 static link하며 PE import audit를 통과합니다. Linux, macOS, Windows용 archive 10개는 하나의 exact release commit에서 build하고 공개 SHA-256과 대조합니다. Build 및 package 검증은 Blackwell 실제 hardware inference 성공을 증명하지 않으므로 해당 archive로 재검증할 때까지 hardware report를 open 상태로 유지합니다. [구현 →](https://github.com/modelscope/FunASR/pull/3570) · [Release →](https://github.com/modelscope/FunASR/releases/tag/runtime-llamacpp-v0.2.6)
|
||||
- 2026/08/30: **v1.4.7 PyPI 공개** — OpenMOSS의 third-party model MOSS-Transcribe-Diarize를 FunASR `AutoModel`에 통합했습니다. local Transformers(`backend="hf"`) 또는 기존 vLLM server(`backend="vllm"`)를 선택할 수 있으며, 두 경로 모두 tag가 포함된 model 출력을 `text`, `raw_text`, millisecond `timestamp`, speaker-aware `sentence_info`로 정규화합니다. MOSS는 한 번의 inference로 transcription, timestamp, speaker diarization을 수행하므로 외부 `vad_model`과 `spk_model`을 지정하지 않습니다. 또한 동일 speaker의 매우 짧은 gap을 잇는 SRT cue 연속성을 개선하고 long session latency 진단용 opt-in realtime decode profiling을 추가했습니다. 업데이트: `python -m pip install -U "funasr==1.4.7"`. GitHub Release에는 검증된 llama.cpp v0.2.5 9개 platform runtime archive도 포함됩니다. [MOSS guide →](./docs/moss_transcribe_diarize.md) · [Release →](https://github.com/modelscope/FunASR/releases/tag/v1.4.7)
|
||||
- 2026/08/29: **llama.cpp runtime v0.2.5** — graph 실행 전에 host weight를 선택한 Vulkan backend buffer에 upload합니다. Q8/F16 weight는 Linux Vulkan llvmpipe에서 검증했으며, Linux, macOS, Windows용 archive 9개를 exact commit `f371370d4c5e4c61d13d4eb9c55cda2f4dd95e4f`에서 build하고 공개 SHA-256과 대조했습니다. AMD Windows hardware crash가 수정됐다고 주장하지 않으며, [#3479](https://github.com/modelscope/FunASR/issues/3479)는 제보자의 실제 hardware 재검증을 위해 open 상태를 유지합니다. [수정 →](https://github.com/modelscope/FunASR/pull/3555) · [Release →](https://github.com/modelscope/FunASR/releases/tag/runtime-llamacpp-v0.2.5)
|
||||
|
||||
@@ -149,6 +149,7 @@ Whisper 是单个模型，**FunASR 是一个工具箱**——按场景挑模型
|
||||
|
||||
## 最新动态
|
||||
|
||||
- 2026/08/30：**v1.4.8 已发布到 PyPI** — 补齐 v1.4.7 之后合入的第三方 MOSS-Transcribe-Diarize vLLM 能力。`backend="vllm"` 现在可接收官方 `response_format=diarized_json` 说话人分段，并统一为 FunASR `sentence_info`；长会议可通过 `max_completion_tokens` 传递生成上限，文档对超过默认 5120-token 边界的录音给出 8192-token 配置。MOSS 仍然端到端完成转写、时间戳和说话人识别，不要外挂 `vad_model` 或 `spk_model`。升级命令：`python -m pip install -U "funasr==1.4.8"`。GitHub 发布页同时提供已验证的 llama.cpp v0.2.6 十平台运行包。[MOSS 部署指南 ->](./docs/moss_transcribe_diarize_zh.md) · [发布页 ->](https://github.com/modelscope/FunASR/releases/tag/v1.4.8)
|
||||
- 2026/08/30：**llama.cpp runtime v0.2.6** — 新增面向 RTX 50 / Blackwell 的 Windows CUDA architecture 120（`sm_120`）专用包，同时保留 architecture 86 包。两个 CUDA ZIP 都包含所需的 NVIDIA cuBLAS DLL 与许可证，静态链接 MSVC runtime，并通过 PE 导入审计；十个 Linux、macOS 与 Windows 压缩包由同一个准确发布提交构建，并按公开 SHA-256 逐一复核。构建与打包通过不代表 Blackwell 实机推理已经验证，因此硬件报告会保持开放，等待用户使用匹配资产复测。[实现 →](https://github.com/modelscope/FunASR/pull/3570) · [发布页 →](https://github.com/modelscope/FunASR/releases/tag/runtime-llamacpp-v0.2.6)
|
||||
- 2026/08/30：**v1.4.7 已发布到 PyPI** — OpenMOSS 的第三方模型 MOSS-Transcribe-Diarize 已接入 FunASR `AutoModel`。可选择本地 Transformers（`backend="hf"`）或已有 vLLM 服务（`backend="vllm"`）；两条路径都会将模型标签输出统一为 `text`、`raw_text`、毫秒级 `timestamp` 和带说话人标签的 `sentence_info`。MOSS 在一次推理中完成转写、时间戳和说话人分离，因此不要再外挂 `vad_model` 或 `spk_model`。本版本还改善了同一说话人极短间隔处的 SRT 字幕连续性，并增加可选的实时解码性能统计，便于定位长会话延迟。升级命令：`python -m pip install -U "funasr==1.4.7"`。GitHub 发布页同时提供已验证的 llama.cpp v0.2.5 九平台运行包。[MOSS 部署指南 →](./docs/moss_transcribe_diarize_zh.md) · [发布页 →](https://github.com/modelscope/FunASR/releases/tag/v1.4.7)
|
||||
- 2026/08/29：**llama.cpp runtime v0.2.5** — 在计算图执行前，将 host 权重上传到所选 Vulkan backend buffer。Q8 与 F16 权重已通过本地 Linux Vulkan llvmpipe 验证，九个 Linux、macOS 与 Windows 压缩包均由准确提交 `f371370d4c5e4c61d13d4eb9c55cda2f4dd95e4f` 构建，并按公开 SHA-256 逐一复核。本版本不宣称 AMD Windows 硬件崩溃已经修复；[#3479](https://github.com/modelscope/FunASR/issues/3479) 保持开放，等待报告者实机复测。[修复 →](https://github.com/modelscope/FunASR/pull/3555) · [发布页 →](https://github.com/modelscope/FunASR/releases/tag/runtime-llamacpp-v0.2.5)
|
||||
|
||||
@@ -5,7 +5,7 @@ This document explains the responsibility boundaries, user entry points, and iss
|
||||
> **Directional roadmap, not a release promise.**
|
||||
> This document records shipped capabilities and active work, but does not commit to
|
||||
> future version numbers or dates. The current Python release is
|
||||
> [`funasr==1.4.7`](https://github.com/modelscope/FunASR/releases/tag/v1.4.7).
|
||||
> [`funasr==1.4.8`](https://github.com/modelscope/FunASR/releases/tag/v1.4.8).
|
||||
> Any future breaking release still requires a maintainer-approved milestone and
|
||||
> migration plan.
|
||||
|
||||
@@ -92,7 +92,7 @@ The four repositories share models and tooling but their responsibility boundari
|
||||
- **Fun-ASR-Nano native Transformers integration** — [huggingface/transformers#46180](https://github.com/huggingface/transformers/pull/46180) is in review; use the PR's exact-head CI and review state as the source of truth.
|
||||
- **Restore complete public checkpoint functionality** — [#3496](https://github.com/modelscope/FunASR/issues/3496) tracks missing CTC tensors needed by timestamp and diarization paths in the Hugging Face checkpoint.
|
||||
- **Realtime concurrency regression** — [#3528](https://github.com/modelscope/FunASR/issues/3528) remains open for reproducible load-test evidence and a bounded fix.
|
||||
- **AMD Windows Vulkan validation** — [#3479](https://github.com/modelscope/FunASR/issues/3479) remains open for reporter hardware retesting against `runtime-llamacpp-v0.2.5`; publication of the archive is not evidence that the hardware crash is fixed.
|
||||
- **AMD Windows Vulkan validation** — [#3479](https://github.com/modelscope/FunASR/issues/3479) remains open for reporter hardware retesting against `runtime-llamacpp-v0.2.6`; publication of the archive is not evidence that the hardware crash is fixed.
|
||||
|
||||
### Next
|
||||
|
||||
|
||||
@@ -4,7 +4,7 @@
|
||||
|
||||
> **方向性路线图，不是版本承诺。**
|
||||
> 本文档记录已交付能力与正在推进的工作，但不承诺未来版本号或日期。当前 Python
|
||||
> 版本是 [`funasr==1.4.7`](https://github.com/modelscope/FunASR/releases/tag/v1.4.7)。
|
||||
> 版本是 [`funasr==1.4.8`](https://github.com/modelscope/FunASR/releases/tag/v1.4.8)。
|
||||
> 任何未来的 breaking release 仍需 maintainer 确认 milestone 与迁移方案。
|
||||
|
||||
---
|
||||
@@ -90,7 +90,7 @@
|
||||
- **Fun-ASR-Nano 的 Transformers 原生集成** —— [huggingface/transformers#46180](https://github.com/huggingface/transformers/pull/46180) 正在审查；以该 PR 的 exact-head CI 与 review 状态为准。
|
||||
- **恢复公开 checkpoint 的完整能力** —— [#3496](https://github.com/modelscope/FunASR/issues/3496) 跟踪 Hugging Face checkpoint 缺少时间戳与说话人路径所需 CTC tensors 的问题。
|
||||
- **实时并发性能回归** —— [#3528](https://github.com/modelscope/FunASR/issues/3528) 保持开放，等待可复现的压测证据与有边界的修复。
|
||||
- **AMD Windows Vulkan 验证** —— [#3479](https://github.com/modelscope/FunASR/issues/3479) 保持开放，等待报告者在 `runtime-llamacpp-v0.2.5` 上进行硬件复测；发布压缩包不等于硬件崩溃已经修复。
|
||||
- **AMD Windows Vulkan 验证** —— [#3479](https://github.com/modelscope/FunASR/issues/3479) 保持开放，等待报告者在 `runtime-llamacpp-v0.2.6` 上进行硬件复测；发布压缩包不等于硬件崩溃已经修复。
|
||||
|
||||
### 下一步
|
||||
|
||||
|
||||
+1
-1
@@ -1 +1 @@
|
||||
1.4.7
|
||||
1.4.8
|
||||
|
||||
@@ -25,6 +25,7 @@ EXPECTED_RUNTIME_ASSET_NAMES = {
|
||||
"funasr-llamacpp-linux-x64.tar.gz",
|
||||
"funasr-llamacpp-macos-arm64.tar.gz",
|
||||
"funasr-llamacpp-windows-x64-avx2.zip",
|
||||
"funasr-llamacpp-windows-x64-cuda-blackwell.zip",
|
||||
"funasr-llamacpp-windows-x64-cuda.zip",
|
||||
"funasr-llamacpp-windows-x64-vulkan.zip",
|
||||
"funasr-llamacpp-windows-x64.zip",
|
||||
@@ -58,6 +59,8 @@ def platform_label(asset_name: str) -> str:
|
||||
return "macOS arm64"
|
||||
if "windows-x64-vulkan" in asset_name:
|
||||
return "Windows x64 Vulkan"
|
||||
if "windows-x64-cuda-blackwell" in asset_name:
|
||||
return "Windows x64 CUDA Blackwell (sm_120)"
|
||||
if "windows-x64-cuda" in asset_name:
|
||||
return "Windows x64 CUDA"
|
||||
if "windows-x64-avx2" in asset_name:
|
||||
|
||||
@@ -341,9 +341,9 @@ def test_top_level_readmes_surface_current_release_and_edge_runtime():
|
||||
}
|
||||
|
||||
for name, text in readmes.items():
|
||||
assert 'python -m pip install -U "funasr==1.4.7"' in text, name
|
||||
assert "https://github.com/modelscope/FunASR/releases/tag/v1.4.7" in text, name
|
||||
assert "runtime-llamacpp-v0.2.5" in text, name
|
||||
assert 'python -m pip install -U "funasr==1.4.8"' in text, name
|
||||
assert "https://github.com/modelscope/FunASR/releases/tag/v1.4.8" in text, name
|
||||
assert "runtime-llamacpp-v0.2.6" in text, name
|
||||
|
||||
assert "https://www.funasr.com/en/deploy/llama-cpp.html" in readmes["README.md"]
|
||||
assert "https://www.funasr.com/deploy/llama-cpp.html" in readmes["README_zh.md"]
|
||||
@@ -356,9 +356,10 @@ def test_top_level_readmes_surface_current_release_and_edge_runtime():
|
||||
"funasr-llamacpp-linux-x64-vulkan.tar.gz",
|
||||
"funasr-llamacpp-windows-x64-vulkan.zip",
|
||||
"funasr-llamacpp-windows-x64-cuda.zip",
|
||||
"funasr-llamacpp-windows-x64-cuda-blackwell.zip",
|
||||
):
|
||||
assert (
|
||||
f"releases/download/runtime-llamacpp-v0.2.5/{asset}" in text
|
||||
f"releases/download/runtime-llamacpp-v0.2.6/{asset}" in text
|
||||
), name
|
||||
assert "releases/download/runtime-llamacpp-v0.2.1/" not in text, name
|
||||
|
||||
@@ -370,9 +371,9 @@ def test_repository_roadmap_tracks_current_delivery_and_open_work():
|
||||
]
|
||||
|
||||
for text in docs:
|
||||
assert "1.4.7" in text
|
||||
assert "1.4.8" in text
|
||||
assert "v1.3.26" not in text
|
||||
assert "runtime-llamacpp-v0.2.5" in text
|
||||
assert "runtime-llamacpp-v0.2.6" in text
|
||||
assert "MOSS-Transcribe-Diarize" in text
|
||||
assert "https://github.com/modelscope/FunASR/issues/3496" in text
|
||||
assert "https://github.com/modelscope/FunASR/issues/3528" in text
|
||||
|
||||
@@ -65,6 +65,11 @@ def runtime_release_payload():
|
||||
"url": "https://example.test/windows-cuda.zip",
|
||||
"digest": sha256_digest("windowscuda"),
|
||||
},
|
||||
{
|
||||
"name": "funasr-llamacpp-windows-x64-cuda-blackwell.zip",
|
||||
"url": "https://example.test/windows-cuda-blackwell.zip",
|
||||
"digest": sha256_digest("windowscudablackwell"),
|
||||
},
|
||||
{
|
||||
"name": "funasr-llamacpp-windows-x64-vulkan.zip",
|
||||
"url": "https://example.test/windows-vulkan.zip",
|
||||
@@ -107,7 +112,7 @@ def test_release_on_tag_workflow_uses_versioned_runtime_pointer():
|
||||
assert "runtime/llama.cpp/current-release.txt" in workflow
|
||||
assert '--runtime-tag "$RUNTIME_TAG"' in workflow
|
||||
assert runtime_pointer.read_text(encoding="utf-8").strip() == (
|
||||
"runtime-llamacpp-v0.2.5"
|
||||
"runtime-llamacpp-v0.2.6"
|
||||
)
|
||||
|
||||
|
||||
@@ -449,7 +454,8 @@ def test_runtime_download_section_lists_all_prebuilt_assets():
|
||||
assert "runtime-llamacpp-v0.1.9" in section
|
||||
assert "attached directly to this Python release" in section
|
||||
assert 'python -m pip install -U "funasr==1.3.26"' in section
|
||||
assert section.count("| [funasr-llamacpp-") == 9
|
||||
assert section.count("| [funasr-llamacpp-") == 10
|
||||
assert "| Windows x64 CUDA Blackwell (sm_120) |" in section
|
||||
assert "Windows x64 CUDA" in section
|
||||
assert "Windows x64 Vulkan" in section
|
||||
assert "Linux x64 Vulkan" in section
|
||||
|
||||
@@ -2,7 +2,7 @@ from pathlib import Path
|
||||
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
RELEASE_VERSION = "1.4.7"
|
||||
RELEASE_VERSION = "1.4.8"
|
||||
README_FILES = ("README.md", "README_zh.md", "README_ja.md", "README_ko.md")
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user