| | --- |
| | language: |
| | - en |
| | library_name: transformers |
| | pipeline_tag: text-generation |
| | tags: |
| | - mathematics |
| | - conjecture-reasoning |
| | - deepseek-math |
| | - lora |
| | base_model: |
| | - deepseek-ai/deepseek-math-7b-instruct |
| | - deepseek-ai/deepseek-math-v2 |
| | datasets: |
| | - NorthernTribe-Research/math-conjecture-training-corpus |
| | --- |
| | |
| | # NorthernTribe-Research/math-conjecture-model |
| |
|
| | An autonomous DeepSeek-Math training and evaluation stack that powers multi-stage Space GPU fine-tuning, quality-gated adapter promotion, and reproducible publishing to your Hugging Face model repository. |
| |
|
| | This folder contains the autonomous training/evaluation stack used by the Space and local runs. |
| |
|
| | ## Included |
| |
|
| | - `configs/deepseek_math.yaml`: DeepSeek-Math baseline preset |
| | - `configs/deepseek_math_v2.yaml`: DeepSeek-Math-V2 baseline preset |
| | - `configs/deepseek_math_sota.yaml`: 4-stage SOTA curriculum + post-eval + quality gate |
| | - `scripts/train_sft.py`: single-stage LoRA/QLoRA SFT |
| | - `scripts/train_sota.py`: staged weighted curriculum with autonomous post-eval and gated push |
| | - `scripts/eval_sota.py`: pass@k + exact/boxed + family/difficulty metrics |
| | - `scripts/merge_and_push.py`: optional adapter merge into full model weights |
| |
|
| | ## Setup |
| |
|
| | ```bash |
| | .venv/bin/python -m pip install -r model_development/requirements.txt |
| | ``` |
| |
|
| | ## Run SOTA curriculum |
| |
|
| | ```bash |
| | .venv/bin/python model_development/scripts/train_sota.py \ |
| | --config model_development/configs/deepseek_math_sota.yaml |
| | ``` |
| |
|
| | Optional controls: |
| |
|
| | ```bash |
| | # Validate stages only |
| | .venv/bin/python model_development/scripts/train_sota.py \ |
| | --config model_development/configs/deepseek_math_sota.yaml \ |
| | --dry-run |
| | |
| | # Force skip quality gate for one run |
| | .venv/bin/python model_development/scripts/train_sota.py \ |
| | --config model_development/configs/deepseek_math_sota.yaml \ |
| | --skip-quality-gate |
| | ``` |
| |
|
| | ## Evaluate adapters |
| |
|
| | ```bash |
| | .venv/bin/python model_development/scripts/eval_sota.py \ |
| | --config model_development/configs/deepseek_math_sota.yaml \ |
| | --adapter-path model_development/runs/math-conjecture-sota/final_adapter \ |
| | --eval-file data/releases/v1/test.parquet \ |
| | --k 6 \ |
| | --max-samples 240 |
| | ``` |
| |
|
| | ## Outputs |
| |
|
| | - final adapter: `model_development/runs/math-conjecture-sota/final_adapter` |
| | - training summary: `model_development/runs/math-conjecture-sota/training_summary.json` |
| | - post-eval report: `model_development/runs/math-conjecture-sota/post_eval_report.json` |
| |
|
| | ## Quality gate behavior |
| |
|
| | When enabled in config/runtime: |
| |
|
| | - validates minimum evaluation coverage |
| | - enforces `pass@1` / `pass@k` thresholds |
| | - enforces required family-level `pass@k` thresholds |
| | - can enforce max final stage `eval_loss` |
| | - blocks hub push if gate fails |
| |
|
| | ## Auth |
| |
|
| | Hub auth resolves from environment first (`HF_TOKEN` / `HUGGINGFACE_HUB_TOKEN`) and can fall back to `huggingface-api-key.json`. |
| |
|
