* Add query expansion model finetuning infrastructure - Training scripts for Qwen3-0.6B and 1.7B models - Dataset generation from s-emanuilov/query-expansion - Evaluation scripts comparing finetuned vs baseline models - GRPO RL training script (optional improvement) - Export script for GGUF conversion Results: - 0.6B finetuned: 95% format compliance (lex/vec/hyde) - Baseline: 0% format compliance - Dataset: 5,157 examples on HuggingFace Hub Models available at: - tobil/qmd-query-expansion-0.6B (recommended) - tobil/qmd-query-expansion-train (dataset) Co-Authored-By: Claude Opus 4.5 * Fix GRPO training script for TRL API compatibility - Use max_completion_length instead of max_new_tokens - Use processing_class instead of tokenizer - Use args instead of config for GRPOTrainer - Add __name__ attribute to reward function class - Accept **kwargs in reward function for extra TRL args - Add new LoRA adapter after merging SFT weights Co-Authored-By: Claude Opus 4.5 * Update README with final evaluation results - 0.6B SFT: 95% format compliance (best) - 0.6B GRPO: 0% (catastrophic forgetting from RL) - 1.7B v2: training completed, evaluation pending - Added GRPO evaluation results Co-Authored-By: Claude Opus 4.5 * Add comprehensive scoring system for query expansion New scoring criteria (0-100 points): - Format (30): Must have lex: and vec: prefixes - Diversity (30): Multiple types, no echoing query, diverse expansions - Hyde (20): Optional, concise, no newlines, no word repetition - Quality (20): Lex=keywords, vec=natural language See SCORING.md for full documentation. Co-Authored-By: Claude Opus 4.5 * Add HuggingFace login and comprehensive scoring to GRPO v2 training - Add explicit HF_TOKEN login before training - Use SCORING.md criteria as RL reward function - Conservative training: LR 1e-6, LoRA rank 4 - Reward scores: good=0.94, bad=0.38 Co-Authored-By: Claude Opus 4.5 * Refactor finetune folder: train/rl scripts with YAML configs Major changes: - train.py: Generic SFT training script using YAML config - rl.py: Generic GRPO training script using YAML config - configs/: YAML configs per training run (sft_v4.yaml, grpo_v4.yaml) - dataset/: Data preparation scripts moved here - tui.py: Interactive model testing interface Training results: - SFT v4: 98.8% avg score (all Excellent) - GRPO v4: 0% (failed - model drifted to verbose explanations) Removed per-model scripts (train_0.6B.py, train_1.7B.py, etc) Co-Authored-By: Claude Opus 4.5 * Add named entity extraction to GRPO reward function Key changes: - Extract named entities (acronyms, proper nouns, technical terms) - Heavy penalty (-30) when lex queries miss named entities - Penalty (-15) for generic filler phrases like "find information about" - Compound entity detection (TDS motorsports -> both words) - Update GRPO config with KL regularization (beta=0.04) - Lower learning rate (5e-7) and add max_steps (200) Test results: - "who is TDS motorsports" good: 1.00, bad: 0.30 (was 0.75) - "how to use React hooks" good: 0.87, bad: 0.45 (was 0.75) Co-Authored-By: Claude Opus 4.5 * Add chat template leakage detection to reward function Zero reward for outputs containing: - <|im_start|>, <|im_end|> tokens - , tags (Qwen3 thinking mode) - Role markers like \nassistant\n, \nuser\n - <|endoftext|> token Co-Authored-By: Claude Opus 4.5 * Strict format validation: every line must be lex:/vec:/hyde: Any line that doesn't start with a valid prefix now returns 0.0 instead of just counting as a penalty. This prevents any prose, explanations, bullet points, or other invalid content. Co-Authored-By: Claude Opus 4.5 * Clean up evaluation files - Remove old versioned evaluation files (0.6B, 1.7B, baseline) - Rename evaluation_v4.json -> evaluation_sft.json - Rename evaluation_v4_grpo.json -> evaluation_grpo_failed.json Co-Authored-By: Claude Opus 4.5 * Refactor evals into separate run and score scripts New structure: - evals/run.py: Generate model outputs to JSONL - evals/score.py: Score outputs with detailed breakdown - evals/queries.txt: Test queries (26 total) Features: - Supports both HF Hub and local model paths - Named entity preservation scoring - Chat template leakage detection - Strict format validation (every line must be lex:/vec:/hyde:) - Generic phrase detection Usage: uv run evals/run.py --model tobil/qmd-query-expansion-0.6B-v4 uv run evals/score.py evals/results_*.jsonl Co-Authored-By: Claude Opus 4.5 * Fix GRPO model loading to use SFT base first The GRPO adapter was trained on merged SFT weights, so loading it directly on the base model results in 0% score. Added --sft-model parameter to evals/run.py to load SFT first, then apply GRPO adapter. With correct loading: GRPO scores 89.7% (all 26 queries Excellent). Updated README with correct GRPO score and loading instructions. Co-Authored-By: Claude Opus 4.5 * Fix TUI to load GRPO models with SFT base first GRPO adapters were trained on merged SFT weights, so they need SFT loaded and merged first before applying the GRPO adapter. Updated MODELS config to include sft_base path for GRPO models, and load_model() now handles the SFT -> merge -> GRPO flow. Co-Authored-By: Claude Opus 4.5 * Update README for unified model repository structure All models (0.6B, 1.7B, 4B) with SFT and GRPO variants now go into a single HuggingFace repo (tobil/qmd-query-expansion) with subfolders for each size and training method. Updated loading examples to show subfolder-based model loading. Co-Authored-By: Claude Opus 4.5 * Update README with separate model repos Changed from subfolder approach to separate repos per model since trainer.push_to_hub() doesn't support subfolder argument. Co-Authored-By: Claude Opus 4.5 * Add 1.7B and 4B GRPO training and GGUF conversion scripts Training scripts for GRPO fine-tuning: - train_1.7B_grpo.py: GRPO training for Qwen3-1.7B - train_4B_grpo.py: GRPO training for Qwen3-4B GGUF conversion scripts: - convert_1.7B_gguf.py: Merge SFT+GRPO adapters and convert to GGUF - convert_4B_gguf.py: Merge SFT+GRPO adapters and convert to GGUF All scripts use PEP 723 inline dependencies for HuggingFace Jobs. Models published: - tobil/qmd-query-expansion-1.7B-sft - tobil/qmd-query-expansion-1.7B-grpo - tobil/qmd-query-expansion-1.7B-gguf - tobil/qmd-query-expansion-4B-sft - tobil/qmd-query-expansion-4B-grpo - tobil/qmd-query-expansion-4B-gguf Co-Authored-By: Claude Opus 4.5 * Remove beads issue tracking Co-Authored-By: Claude Opus 4.5 * Remove beads reference from CLAUDE.md Co-Authored-By: Claude Opus 4.5 * Fix GRPO reward function to handle think blocks and end tokens - Strip <|im_end|> token from completions (model output includes it) - Change think_penalty to skipped_think bonus (+20 for not using think) - Adjust max_possible to account for bonus (120/140) - Fix typo in chat template artifact check Co-Authored-By: Claude Opus 4.5 * Make TUI model list dynamic from HuggingFace Hub - Fetch available qmd-query-expansion models from tobil/ on Hub - Auto-detect model size (0.6B, 1.7B, 4B) and use correct base model - Group models by type (SFT vs GRPO) in menu - Skip GGUF repos in model listing Co-Authored-By: Claude Opus 4.5 * Fix GRPO training: apply chat template to prompts The SFT model was trained with chat template format but GRPO was passing raw prompts. Now prompts are formatted with tokenizer.apply_chat_template() so the model sees the same format it learned during SFT. Also update extract_query_from_prompt to strip chat template artifacts. Co-Authored-By: Claude Opus 4.5 * Finetune 2.0: consolidate and simplify the entire training pipeline Consolidate ~2,800 lines of duplicated code across 12 files into 5 clean, well-documented files targeting Qwen3-1.7B end-to-end. Key changes: - Extract reward function into single source of truth (reward.py) Previously duplicated 3x with divergent bugs across rl.py, train_1.7B_grpo.py, and train_4B_grpo.py - Unify training into one script with sft/grpo subcommands (train.py) Replaces train.py + rl.py + train_1.7B_grpo.py + train_4B_grpo.py - Merge eval generate+score into single eval.py Replaces evals/run.py + evals/score.py - Parameterize GGUF conversion by --size (convert_gguf.py) Replaces convert_1.7B_gguf.py + convert_4B_gguf.py - Fix critical bug: rl.py silently ignored beta/temperature from config, causing the exact catastrophic drift its own comments warned about - Fix prompt consistency: all files use /no_think chat template format - Retarget configs from 0.6B to 1.7B - Comprehensive README documenting the full pipeline Removed: rl.py, train_1.7B_grpo.py, train_4B_grpo.py, convert_1.7B_gguf.py, convert_4B_gguf.py, tui.py, evals/run.py, evals/score.py Net: -3,429 lines, +382 lines Co-Authored-By: Claude (claude-fudge-eap-cc) * Add HF Jobs scripts, temporal query examples, and training results - jobs/sft.py and jobs/grpo.py: self-contained scripts for `hf jobs uv run` (no local GPU needed) - 12 temporal/recency query examples in training data (e.g. "recent news about Shopify" -> lex with years 2025/2026) - 4 temporal test queries in evals/queries.txt - README updated with HF Jobs workflow, training results, and updated file structure - Remove .beads tracking SFT and GRPO successfully trained on A10G via HF Jobs: SFT: eval loss 0.321, token accuracy 92.4% GRPO: mean reward 0.757, 200 steps, KL 0.00048 Co-Authored-By: Claude (claude-fudge-eap-cc) * Deploy fine-tuned GRPO model as default for query expansion Switch from generic Qwen3-1.7B-Q8_0 (~2.2GB) to fine-tuned qmd-query-expansion-1.7B-q4_k_m (~1.1GB). The fine-tuned Q4 scores 91.7% avg with 30/30 Excellent, outperforming the base Q8. - Update default generate model in src/llm.ts - Update README model table, architecture diagram, config block - Add v2 training data, eval scripts, and quantize job - Remove superseded v1 training data (5,742 → 1,000 examples) - Update finetune README with v2 results and file structure Co-Authored-By: Claude (claude-fudge-eap-cc) --------- Co-authored-by: Claude Opus 4.5