feat: Apple Silicon (MLX) support for query expansion fine-tuning

.gitignore

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 finetune/outputs/
 finetune/data/train/
 .claude/
+
+# finetune-mlx artifacts (local runs)
+finetune-mlx/*.nohup.log
+finetune-mlx/eval_results.json

finetune-mlx/.gitignore

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+# Training artifacts (large files)
+adapters/
+models/
+exports/merged/
+exports/*.gguf
+
+# Keep Modelfiles (small, useful)
+!exports/*.Modelfile
+
+# Python
+.venv/
+__pycache__/
+*.pyc
+
+# Logs
+*.log
+*.nohup.log
+
+# Data (downloaded separately)
+data/
+eval_results.json

finetune-mlx/README.md

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+# QMD Query Expansion - Apple Silicon (MLX)
+
+Apple Silicon alternative to the CUDA-based [`finetune/`](../finetune/) directory.
+
+Port of QMD's query expansion fine-tuning to Apple Silicon using [MLX](https://github.com/ml-explore/mlx).
+
+Train small language models locally on M1/M2/M3/M4 Macs to expand search queries for hybrid retrieval.
+
+## Features
+
+- **SFT Training**: Supervised fine-tuning with LoRA
+- **GRPO Training**: Group Relative Policy Optimization (reinforcement learning)
+- **100% Local**: No cloud GPU needed, runs on Apple Silicon
+- **MLX Optimized**: Native Metal acceleration
+
+## Results
+
+Comparison with original NVIDIA A10G implementation:
+
+| Metric | NVIDIA (SFT+GRPO) | Apple Silicon (SFT) | Apple Silicon (GRPO) |
+|--------|-------------------|---------------------|----------------------|
+| Avg Score | 92% | 99.6% | 100.4% |
+| Perfect Queries | 30/30 | 28/30 | 28/30 |
+| Hardware | A10G 24GB | Mac Mini M4 | Mac Mini M4 |
+| Cost | ~$2/run | $0 | $0 |
+
+## Quick Start
+
+```bash
+# Setup
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+
+# Download and convert base model
+python -c "from mlx_lm import load; load('Qwen/Qwen3-1.7B')"
+
+# Train SFT (supervised fine-tuning)
+python train.py sft --iters 3500
+
+# Train GRPO (reinforcement learning refinement)
+python grpo.py --steps 200
+
+# Evaluate
+python grpo.py --eval-only --adapter adapters/qwen3-grpo
+```
+
+## What It Does
+
+Given a query like `"auth config"`, the model produces structured expansions:
+
+```
+lex: authentication configuration
+lex: auth settings setup
+vec: how to configure authentication settings
+hyde: Authentication can be configured by setting AUTH_SECRET...
+```
+
+These feed into QMD's hybrid retrieval:
+- `lex:` → BM25 full-text search
+- `vec:` → Vector similarity search  
+- `hyde:` → Hypothetical document embedding
+
+## File Structure
+
+```
+├── train.py          # SFT training script
+├── grpo.py           # GRPO (RL) training script
+├── eval.py           # Evaluation utilities
+├── reward.py         # Scoring/reward function
+├── convert.py        # GGUF conversion for Ollama
+├── configs/
+│   └── sft.yaml      # SFT hyperparameters
+├── evals/
+│   └── queries.txt   # Test queries (31 total)
+└── tests/            # Unit tests
+```
+
+## Requirements
+
+- macOS with Apple Silicon (M1/M2/M3/M4)
+- Python 3.10+
+- ~8GB RAM for training
+- ~4GB disk for models
+
+## Training Details
+
+### SFT (Supervised Fine-Tuning)
+- Base model: Qwen3-1.7B
+- LoRA rank: 8, layers: 8
+- Learning rate: 1e-4
+- Steps: 3500
+- Time: ~60 min on M4
+
+### GRPO (Group Relative Policy Optimization)
+- Starts from SFT checkpoint
+- 4 completions per query
+- KL regularization (β=0.04)
+- Steps: 200
+- Time: ~30 min on M4
+
+## Credits
+
+- Original QMD: [tobi/qmd](https://github.com/tobi/qmd)
+- MLX framework: [ml-explore/mlx](https://github.com/ml-explore/mlx)
+- Base model: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
+
+## Contributors
+
+- [@sujito00](https://github.com/sujito00)
+- [@dgilperez](https://github.com/dgilperez)
+
+## License
+
+MIT

finetune-mlx/configs/sft.yaml

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+# SFT Training Config for QMD Query Expansion (Apple Silicon)
+
+model:
+  base: "Qwen/Qwen3-1.7B"
+  output: "qmd-query-expansion-1.7B-sft"
+
+dataset:
+  name: "tobil/qmd-query-expansion-train-v2"
+  text_field: "text"
+  eval_split: 0.1
+
+training:
+  batch_size: 4
+  iters: 3000
+  learning_rate: 2e-4
+  max_length: 512
+  grad_accumulation_steps: 4
+
+lora:
+  num_layers: 16
+  rank: 16