Native Go bindings for MLX-C gradient computation on Apple Silicon.
Foundation for LoRA training without Python.
- VJP (reverse-mode autodiff) for backward pass
- JVP (forward-mode autodiff) for directional derivatives
- ValueAndGrad for combined loss + gradient computation
- Checkpoint for memory-efficient gradient recomputation
- CrossEntropyLoss (numerically stable via LogSumExp)
- MSELoss, Log, SumAll, MeanAll, OnesLike helpers
- TakeAlongAxis and LogSumExp ops
- Fix closure callback null vector bug (affects compile.go too)
- Fix Float() returning 0 for float32 arrays
14 tests passing on Metal GPU.
Co-Authored-By: Virgil <virgil@lethean.io>
Remove the manual -tags mlx requirement. MLX is now automatically
compiled on darwin/arm64 via build constraints. Stubs remain for
other platforms. No functional change.
Co-Authored-By: Virgil <virgil@lethean.io>
LEM scoring pipeline, native MLX Metal bindings, Claude SDK wrapper,
RAG with Qdrant/Ollama, unified AI facade, and MCP protocol server.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
