QMD Query Expansion Fine-Tuning

Overview

Train Qwen3-1.7B to expand search queries into structured hyde:/lex:/vec: output for QMD's hybrid retrieval pipeline.

Output Format

hyde: A hypothetical document passage that would answer the query.
lex: keyword1
lex: keyword2
vec: semantic query reformulation
vec: another semantic variation

• hyde: always comes FIRST (one line max)
• lex: lines for BM25 keyword search (1-3 lines, short keywords)
• vec: lines for vector similarity search (1-3 lines, natural language)

Training Data Format

There is exactly one JSONL format. Every file in data/*.jsonl must match the strict Pydantic schema in dataset/schema.py:

{"query": "auth config", "output": [["hyde", "..."], ["lex", "..."], ["vec", "..."]]}

• query: non-empty string
• output: list of [type, text] pairs where type is "lex", "vec", or "hyde"
• Extra metadata fields (category, intent, is_short) are allowed but ignored

The schema is enforced by dataset/schema.py:TrainingExample (Pydantic model). All data loading goes through load_examples() which fails loudly on invalid data. No format alternatives, no legacy fallbacks.

All .jsonl files in data/ are concatenated and deduplicated for training runs. The prepared train/val files in data/train/ are ephemeral build artifacts.

HuggingFace Repositories

Repository	Purpose
tobil/qmd-query-expansion-1.7B	Final merged model (SFT baseline)
tobil/qmd-query-expansion-1.7B-gguf	GGUF quantized versions for deployment
tobil/qmd-query-expansion-1.7B-sft	SFT adapter checkpoint (intermediate)
tobil/qmd-query-expansion-train	Prepared training dataset
tobil/qmd-query-expansion-1.7B-grpo	Experimental GRPO adapter (optional)

Rules:

• No versioned repos (-v1, -v2, -v4, etc.) - update in place
• Only push when eval scores improve over current deployed model
• Always include eval results in model card when pushing

Dataset Tools

Script	Purpose
dataset/schema.py	Pydantic TrainingExample model + load_examples()
dataset/prepare_data.py	Load via schema, apply Qwen3 chat template, dedup, split
dataset/validate_schema.py	Validate all JSONL files against schema
dataset/score_data.py	Score all examples using reward.py
dataset/analyze_data.py	Analyze distribution and quality

Training Pipeline

Always use Qwen3-1.7B as the base model unless explicitly stated otherwise.

Stage 0: Prepare Data

uv run dataset/prepare_data.py
# Creates: data/train/train.jsonl, data/train/val.jsonl (ephemeral)

Stage 1: SFT

# Local (requires CUDA)
uv run train.py sft --config configs/sft.yaml

# Cloud (HuggingFace Jobs)
hf jobs uv run --flavor a10g-large --secrets HF_TOKEN --timeout 2h jobs/sft.py

Stage 2: (Experimental) GRPO

# Experimental script
cd finetune && HF_TOKEN=${HF_TOKEN} uv run python experiments/grpo/grpo.py

HuggingFace Jobs

hf jobs ps                    # List running jobs
hf jobs logs <job-id>         # Stream logs
hf jobs inspect <job-id>      # Check status
hf jobs cancel <job-id>       # Cancel a job

Evaluation

uv run eval.py ./outputs/sft
uv run eval.py tobil/qmd-query-expansion-1.7B
uv run eval.py ./outputs/sft -o eval_results.json

Quality Scoring

reward.py is the single source of truth for scoring:

uv run reward.py   # Self-test

See SCORING.md for the full rubric.

Experiments

Experimental training configurations live in experiments/:

experiments/
├── lfm2/          # LiquidAI LFM2-1.2B (hybrid architecture, faster inference)
│   ├── sft_lfm2.yaml
│   └── sft_lfm2.py
├── grpo/          # Experimental GRPO recipe and config
│   ├── grpo.py
│   └── grpo.yaml
└── gepa/          # DSPy-based prompt optimization (GEPA)
    ├── dspy_gepa.py
    └── ...

These are not part of the main training pipeline.

Key Files

finetune/
├── reward.py          # Scoring function (single source of truth)
├── train.py           # SFT training entrypoint
├── eval.py            # Generate and score expansions
├── convert_gguf.py    # GGUF conversion
├── SCORING.md         # Detailed scoring rubric
├── CLAUDE.md          # This file
├── Justfile           # Common commands
├── data/              # All training JSONL files (strict schema)
├── dataset/           # Schema + data tools (Pydantic-based)
├── jobs/              # Self-contained HuggingFace Jobs scripts
├── configs/           # Training configs (sft.yaml)
├── evals/             # Test queries
├── experiments/       # Experimental configs (LFM2, GEPA, GRPO)
└── outputs/           # Local training outputs (gitignored)