---
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`.