bf7480d042
Release 2.0.4-beta.9: Audio transcription via mlx-audio
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Major Features:
- Audio transcription via mlx-audio backend (Whisper, >10min duration)
- OpenAI /v1/audio/transcriptions endpoint
- Memory Gate System (Vision: 8GB, Audio: 4GB)
- Config-based backend routing (ADR-020)
- Benchmark toolchain (memmon/memplot, Schema v0.2.2)
Key Fixes:
- EuroLLM tokenizer decoding
- Vision-model text-only routing regression
- Multimodal model context length detection
- Memory cleanup bug (mx.metal.clear_cache)
- Orphan process bug
Test Results:
- Unit tests: 647 passed, 11 skipped (Python 3.10-3.12)
- wet-umbrella: 171 passed total
See CHANGELOG.md for complete details and known issues.
2026-02-04 03:10:30 +01:00
d3f7d091bc
Release 2.0.4-beta.3: Dependency compatibility + Documentation
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Bugfixes and compatibility improvements. No new features.
Core fixes:
- Framework detection for web API models (Issue #48 )
- Video-only model filtering from vision capability
- Page size detection for memory metrics (macOS)
- Model switch log timing (after load completion)
Compatibility:
- hub 1.x + transformers 5.0 support
- Python 3.9-3.14 verified (494 tests passing)
Testing infrastructure:
- Benchmark schema v0.2.0 (hardware profiling, system health)
- Benchmark template v1.0 (automated JSONL→Markdown reports)
- Memory timeline visualization (memplot.py)
- Unified model filter (build_model_object single source)
Documentation:
- Multi-Modal Support section in README (Vision subsection)
- JSON API 0.1.5-0.1.6 marked Stable
- Vision promoted from alpha to beta status
- Removed conceptual drift and outdated references
See CHANGELOG.md for complete details.
2025-12-23 12:19:04 +01:00
86f669dc82
Release 2.0.4-beta.1: Vision + Pipes + Memory
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- Vision Support (Issue #45 ): CLI + Server with OpenAI-compatible image API, EXIF metadata
- Unix Pipes (ADR-014): stdin support, isatty detection, SIGPIPE handling
- Memory-Aware Loading (ADR-016): Pre-load checks with >70% RAM warnings
- Python 3.9-3.14: Full compatibility verified (476-485 tests passing)
- Fixed: --log-json regression (Issue #44 ), Vision multimodal history filtering
See CHANGELOG.md for complete details.
2025-12-16 19:35:30 +01:00
d32d3185dd
Release 2.0.2: Test infrastructure hardening & empirical validation
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Stable release completing Issue #32 recovery plan - all tests passing.
Bug Fixes:
- Test collection regression (E2E suite parametrization)
- Stop token ordering (batch + streaming modes)
- E2E test temperature flakiness (deterministic sampling)
- Web API framework detection (PR #42 by @limey, fixes #41 )
- E2E test marker fix (show_model_portfolio diagnostics)
Architecture:
- mlx-lm API evaluation: Keep manual text-based implementation
- Stop token workarounds: All 3 validated (Phi-3, DeepSeek-R1, GPT-oss)
Testing:
- Portfolio Discovery: 73/81 tests, 17 models, 0 failures
- E2E infrastructure hardened (TOKENIZERS, polling, gc.collect())
- Multi-Python validation: 3.9-3.13 passing
Documentation:
- ADR-009 Outstanding Work completed + Implementation Plan removed
- TESTING-DETAILS.md: Portfolio Discovery + E2E Architecture updated
- CHANGELOG.md: Complete 2.0.2 stable release notes
2025-11-15 22:10:08 +01:00
fb54f59cd4
Release 2.0.0-beta.6: Stop token & compatibility bug fixes
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Fixes Issue #32 (generic multi-EOS detection) and Issue #37 (model detection)
- Generic stop token detection: Multi-EOS models (MXFP4, Qwen, Llama) now use eos_token_ids Set instead of
model-specific workarounds
- Private/org MLX model detection: `mlxk run` now works outside `mlx-community/*` namespace
- Commit-pinned compatibility checks: Models with `@commit_hash` validated before inference
- Packaging dependencies: Fixed `pip install -e .` requirements
- ADR-009: Stop Token Detection Fix (generic approach + test strategy)
- ADR-011: E2E Live Test Architecture (planned)
See CHANGELOG.md and TESTING.md for details.
2025-10-24 15:46:42 +02:00
f5fe1dd061
Release 2.0.0-beta.5: Enhanced error handling & bug fixes
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Features:
- Enhanced error handling & logging (ADR-004): Unified error envelope, structured logging with JSON support, request correlation
- Legacy format detection (Issue #37 ): Runtime compatibility check for weight file formats
Bug Fixes:
- Issue #37 : Models with legacy weight formats now correctly detected as runtime-incompatible
- CLI regression fix: mlxk2 without arguments shows help instead of JSON error
Test Status: 295/295 passed, 14 skipped
2025-10-21 00:24:47 +02:00
9261bc0c4e
2.0.0-beta.3: Feature Complete - Clone Implementation & Issue Resolution
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- Clone Feature (Issue #29 ): Complete workspace-based workflow with ADR-007
- Pull Preflight (Issue #30 ): Prevents cache pollution from gated/private repos
- Lenient MLX Detection (Issue #31 ): Framework detection beyond mlx-community
- Multi-shard Health (Issue #27 ): Strict completeness validation
- Full JSON API 0.1.4: Complete schema for all 10 commands
- Test Suite: 254/254 passed, comprehensive validation
See CHANGELOG.md fnd TESTING.md or technical implementation details.
2025-09-18 14:09:32 +02:00
57bf6d86be
2.0.0-beta.3: Feature Complete - Full 1.1.1 Parity Achieved
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Major Features Added:
• Complete run command implementation with interactive/single-shot modes
• MLXRunner core engine ported from 1.x with modular architecture
• OpenAI-compatible server with SIGINT-robust supervisor mode
• Experimental push feature properly isolated behind environment variable
Key Improvements:
- Full feature parity with 1.1.1 stable releases
- Enhanced human output formatting across all commands
- Clean separation of stable (184 tests) vs experimental features
- Updated demo GIF showcasing improved 2.0 interface
Fixes:
- Pull operation cache pollution (Issue #30 ) with preflight access checks
- Test stability improvements across all environments
Architecture:
- Modular runner design with focused helper modules
- Thread-safe model loading and memory management
- stable testing across Python 3.9-3.13
Ready for use as comprehensive 1.x alternative.
2025-09-14 18:04:18 +02:00
19a66674c0
2.0.0-alpha.1: human output default; strict health ( #27 , PyTorch index)
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See CHANGELOG.md and README.md
2025-08-31 22:25:43 +02:00
d603cde9d5
MLX-Knife 2.0 Session 1: JSON-First Foundation
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✅ FOUNDATION COMPLETE: Working `python -m mlxk2.cli list` command
Architecture:
- Clean-room implementation (145 lines total)
- JSON-first design for broke-cluster automation
- Modular structure: core/operations/output separation
Deliverables:
- mlxk2/core/cache.py: Cache path management (35 lines)
- mlxk2/operations/list.py: Model discovery with JSON output (41 lines)
- mlxk2/cli.py: CLI entry point with error handling (69 lines)
- docs/: Complete ADR and implementation plan documentation
Success Metrics Achieved:
✅ 12 models detected and JSON-formatted
✅ Consistent schema: status/command/data/error
✅ broke-cluster ready: `jq -r '.data.models[].name'`
✅ 4x faster than planned (1 hour vs 4 hour target)
Next: Session 2 - health, pull, rm operations
2025-08-26 19:25:50 +02:00
The BROKE Cluster Team