Ports all remaining LEM pipeline commands from pkg/lem into core ml,
eliminating the standalone LEM CLI dependency. Each command is split
into reusable business logic (pkg/ml/) and a thin cobra wrapper
(internal/cmd/ml/).
New commands: query, inventory, metrics, ingest, normalize, seed-influx,
consolidate, import-all, approve, publish, coverage.
Adds Path(), Exec(), QueryRowScan() convenience methods to DB type.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Tracks model size at load time and checks Metal active memory after
each generation. If usage exceeds 3× model size, forces double GC
and cache clear as a safety net.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Go GC cannot see Metal/C memory pressure, so intermediate arrays from
each forward pass accumulated without bound, causing OOM kills after
3-4 requests. Fix: runtime.SetFinalizer on every Array releases C
handles when GC collects them, and runtime.GC() is forced every 4
tokens during generation. Also adds SetMemoryLimit(24GB) as a hard
Metal ceiling.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add ClearCache() wrapping mlx_clear_cache
- Clear Metal allocator cache every 8 tokens during generation
- Set 16GB cache limit on backend init
- Prevents GPU memory from growing unbounded during inference
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
CGo wrapper for mlx-c providing zero-Python Metal GPU inference.
Includes Gemma 3 model architecture, BPE tokenizer, KV cache,
composable sampling, and OpenAI-compatible serve command.
Build-tagged (darwin && arm64 && mlx) with stubs for cross-platform.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Port LEM scoring/training pipeline into CoreGo as pkg/ml with:
- Inference abstraction with HTTP, llama-server, and Ollama backends
- 3-tier scoring engine (heuristic, exact, LLM judge)
- Capability and content probes for model evaluation
- GGUF/safetensors format converters, MLX to PEFT adapter conversion
- DuckDB integration for training data pipeline
- InfluxDB metrics for lab dashboard
- Training data export (JSONL + Parquet)
- Expansion generation pipeline with distributed workers
- 10 CLI commands under 'core ml' (score, probe, export, expand, status, gguf, convert, agent, worker)
- 5 MCP tools (ml_generate, ml_score, ml_probe, ml_status, ml_backends)
All 37 ML tests passing. Binary builds at 138MB with all commands.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
