---
title: Math Conjecture Trainer
sdk: gradio
sdk_version: "6.6.0"
python_version: "3.10"
app_file: app.py
pinned: false
emoji: 🧮

---

# Math Conjecture Trainer Space

An autonomous training operations console for DeepSeek-Math that runs multi-stage curriculum fine-tuning on Space GPU, executes post-training quality evaluation, and publishes only qualified adapters, checkpoints, and run reports to your Hugging Face model repository.

This Space is the tactical operations console for `maths-conjuncture-solutions` and is wired to:

- dataset: `NorthernTribe-Research/math-conjecture-training-corpus`
- model repo: `NorthernTribe-Research/math-conjecture-model`

## End-to-end flow

1. Download released parquet splits (`train/validation/test`).
2. Build runtime config from `configs/deepseek_math_sota.yaml`.
3. Run 4-stage curriculum LoRA fine-tuning with `scripts/train_sota.py`.
4. Run post-train evaluation (`pass@1`, `pass@k`, exact/boxed, family metrics).
5. Apply quality gate thresholds before hub push.
6. Emit `training_summary.json` + `post_eval_report.json` and stream live telemetry in UI.

## Access posture

Credentials and publish permissions are handled by deployment runtime settings.

## Runtime controls

- `Autonomous Mode`: enabled by default; applies full-stage training/eval/gate/publish profile automatically.
- `Run Evaluation After Training`: toggles post-train eval in runtime config.
- `Enforce Quality Gate`: enables/disables promotion gate checks.
- `Gate Min pass@1`, `Gate Min pass@k`, `Gate Min Rows`: runtime gate thresholds.
- `Live Tactical Telemetry`: real-time stage progression, runtime posture, and training-loss graph (sparkline) with gate/push state.
- `Ops Console (Live Log + Mission JSON)`: unified panel for line-by-line runtime stream, heartbeats, and structured mission summary.
- `Validation Mode (No Training)`: validates pipeline with `--dry-run`.
- `Force Dataset Redownload`: bypasses cached parquet files.
- `Abort Active Run`: cancels active subprocess tree.

## Artifacts

- runtime config: `workspace/runtime/deepseek_math_sota.runtime.yaml`
- run output root: `workspace/runs/math-conjecture-sota`
- final adapter: `workspace/runs/math-conjecture-sota/final_adapter`
- training summary: `workspace/runs/math-conjecture-sota/training_summary.json`
- post-eval report: `workspace/runs/math-conjecture-sota/post_eval_report.json`

## Notes

- Full training runs on GPU when available and automatically falls back to CPU mode when CUDA is unavailable.
- App handles Gradio copy-button compatibility across versions automatically.