lex: authentication configuration
lex: auth settings setup
vec: how to configure authentication settings
vec: authentication configuration options
hyde: Authentication can be configured by setting the AUTH_SECRET environment variable.

These feed into QMD's three search backends:

lex: lines go to BM25 full-text search (short, keyword-focused)
vec: lines go to vector similarity search (natural language phrases)
hyde: is a hypothetical document passage for embedding-based retrieval (HyDE technique)

Quick Start

Cloud training via HuggingFace Jobs (no GPU needed)

bash

# 1. SFT: teach the model the output format (~45 min on A10G, ~$1.50)
hf jobs uv run --flavor a10g-large --secrets HF_TOKEN --timeout 2h jobs/sft.py

# 2. GRPO: RL refinement on top of SFT (~20 min on A10G, ~$0.50)
hf jobs uv run --flavor a10g-large --secrets HF_TOKEN --timeout 4h jobs/grpo.py

# 3. Evaluate against test queries (needs local GPU or use eval job)
uv run eval.py --model tobil/qmd-query-expansion-1.7B-grpo \
               --sft-model tobil/qmd-query-expansion-1.7B-sft

# 4. Convert to GGUF for local deployment (Ollama, llama.cpp)
uv run convert_gguf.py --size 1.7B

Local training (if you have a GPU)

bash

uv run train.py sft  --config configs/sft.yaml
uv run train.py grpo --config configs/grpo.yaml

Monitoring HF Jobs

bash

hf jobs ps                           # list running jobs
hf jobs inspect <job-id>             # check status
hf jobs logs <job-id>                # stream logs
hf jobs cancel <job-id>              # cancel a job

Prompt Format

All tools use the same prompt — Qwen3 chat template with /no_think:

<|im_start|>user
/no_think Expand this search query: {query}<|im_end|>
<|im_start|>assistant

The /no_think directive suppresses Qwen3's chain-of-thought mode, producing direct lex:/vec:/hyde: output without <think> blocks.

File Structure

finetune/
├── reward.py          # Scoring/reward function (single source of truth)
├── train.py           # Unified SFT + GRPO training (two subcommands)
├── eval.py            # Generate expansions and score them
├── convert_gguf.py    # GGUF conversion for Ollama/llama.cpp
├── jobs/
│   ├── sft.py         # Self-contained SFT for HuggingFace Jobs
│   ├── grpo.py        # Self-contained GRPO for HuggingFace Jobs
│   ├── eval.py        # Self-contained eval for HuggingFace Jobs
│   ├── eval_common.py # Shared eval utilities
│   └── quantize.py    # GGUF quantization for HuggingFace Jobs
├── configs/
│   ├── sft.yaml       # SFT hyperparameters for Qwen3-1.7B
│   └── grpo.yaml      # GRPO hyperparameters for Qwen3-1.7B
├── evals/
│   └── queries.txt    # 31 test queries across 8 categories
├── data/
│   └── qmd_expansion_v2.jsonl  # Source training data (1,000 high-quality examples)
├── dataset/
│   ├── generate_data.py         # Generate data via Claude API
│   ├── generate_data_offline.py # Generate from existing HF dataset
│   ├── prepare_data.py          # Format for Qwen3 chat template
│   └── clean_data.py            # Detect technical term misinterpretations
├── SCORING.md         # Detailed scoring rubric reference
└── README.md          # This file

Training Pipeline

Stage 1: SFT (Supervised Fine-Tuning)

Teaches the model the lex:/vec:/hyde: output format from labeled examples.

Parameter	Value
Base model	`Qwen/Qwen3-1.7B`
Method	LoRA (rank 16, alpha 32)
Target modules	All projection layers (q/k/v/o/gate/up/down)
Dataset	~2,290 examples (train split)
Effective batch size	16 (4 × 4 gradient accumulation)
Epochs	5
Learning rate	2e-4 (cosine schedule)

bash

uv run train.py sft --config configs/sft.yaml
uv run train.py sft --config configs/sft.yaml --dry-run  # preview config

Stage 2: GRPO (Group Relative Policy Optimization)

Reinforcement learning on top of the merged SFT weights. The model generates multiple expansions per query, they are scored by the reward function, and the model is updated to prefer higher-scoring outputs.

Parameter	Value
Base	Merged SFT checkpoint
Method	LoRA (rank 4, alpha 8) — smaller for RL stability
Target modules	q_proj, v_proj only
Reward	`reward.py` (rule-based, 5 dimensions)
KL beta	0.04 — prevents drift from SFT checkpoint
Generations per prompt	4
Max steps	200
Learning rate	5e-7

Important: beta > 0 is critical. With beta=0 the model experiences catastrophic drift and scores drop to 0%.

bash

uv run train.py grpo --config configs/grpo.yaml
uv run train.py grpo --config configs/grpo.yaml --dry-run  # test reward function

Evaluation

eval.py generates expansions from a model and scores them against test queries:

bash

# Evaluate an SFT model
uv run eval.py --model tobil/qmd-query-expansion-1.7B-sft

# Evaluate a GRPO model (needs SFT adapter merged first)
uv run eval.py --model tobil/qmd-query-expansion-1.7B-grpo \
               --sft-model tobil/qmd-query-expansion-1.7B-sft

# Verbose output with deduction details
uv run eval.py --model tobil/qmd-query-expansion-1.7B-sft -v

# Save detailed scores to JSON
uv run eval.py --model tobil/qmd-query-expansion-1.7B-sft -o scores.json

# Score an existing JSONL file (backwards compat with old run.py output)
uv run eval.py --score-only evals/results_old.jsonl

Reward Function

reward.py is the single source of truth for scoring. It is used both as the GRPO reward signal during training and for evaluation.

Five scoring dimensions (max 120 without hyde, 140 with):

Dimension	Points	What It Measures
Format	0-30	Has lex/vec lines, no invalid lines
Diversity	0-30	Multiple expansion types, diverse content, no query echoes
HyDE	0-20	Present, 50-200 chars, single line, not repetitive
Quality	0-20	Lex shorter than vec, natural language, preserves key terms
Entity	-45 to +20	Named entities preserved in lex and vec lines
Think bonus	0-20	Reward for NOT using `<think>` mode

Hard failures (instant 0.0):

Chat template leakage (<|im_start|>, <|im_end|>, etc.)
Any line without a valid lex:, vec:, or hyde: prefix

bash

# Self-test the reward function
uv run reward.py

GGUF Conversion

Merges base + SFT + GRPO adapters into a single model and produces quantized GGUF files for deployment:

bash

# Use preset for 1.7B
uv run convert_gguf.py --size 1.7B

# Use preset for 4B
uv run convert_gguf.py --size 4B

# Custom models
uv run convert_gguf.py --base Qwen/Qwen3-1.7B \
                       --sft tobil/qmd-query-expansion-1.7B-sft \
                       --grpo tobil/qmd-query-expansion-1.7B-grpo \
                       --output tobil/qmd-query-expansion-1.7B-gguf

Using with Ollama

bash

huggingface-cli download tobil/qmd-query-expansion-1.7B-gguf \
    qmd-query-expansion-1.7B-q4_k_m.gguf --local-dir .

echo 'FROM ./qmd-query-expansion-1.7B-q4_k_m.gguf' > Modelfile
ollama create qmd-expand -f Modelfile
ollama run qmd-expand

Data Pipeline

The training data (1,000 examples in data/qmd_expansion_v2.jsonl) was generated from two sources and cleaned for quality. To regenerate:

bash

# Generate from existing HuggingFace dataset (bulk, no API needed)
uv run dataset/generate_data_offline.py

# Generate via Claude API (higher quality, needs ANTHROPIC_API_KEY)
uv run dataset/generate_data.py --count 100

# Detect and fix technical term misinterpretations
uv run dataset/clean_data.py

# Format for Qwen3 chat template, add short-query augmentation, split train/val
uv run dataset/prepare_data.py

Architecture Notes

The two-stage training approach (SFT → GRPO) is standard for structured-output models:

SFT establishes format compliance and basic query understanding. It uses a large LoRA (rank 16, all projection layers) because it needs to learn a new output format from scratch.
GRPO refines quality within the learned format. It uses a small LoRA (rank 4, q/v only) and KL regularization to make incremental improvements without losing what SFT taught.

The reward function is entirely rule-based (no LLM judge) which makes it fast, deterministic, and suitable as an RL signal. See SCORING.md for the full rubric.

Training Results (Qwen3-1.7B, v2)

SFT

Metric	Value
Final train loss	0.472
Final eval loss	0.304
Token accuracy (train)	97.4%
Token accuracy (eval)	93.8%
Epochs	5
Hardware	A10G (24 GB VRAM)

GRPO

Metric	Value
Mean reward	0.757
Final loss	0.0005
KL divergence	0.00048
Mean completion length	~58 tokens
Training time	~19 min (200 steps)
Hardware	A10G (24 GB VRAM)

Evaluation Scores

Model	Average Score	Excellent (30)
SFT	92.0%	30/30
GRPO	91.7%	30/30