---
language: [en]
license: apache-2.0
tags:
- gpt2
- physics
- ibdp
- education
- tutor
datasets:
- custom
widget:
- text: "Explain Newton’s second law for IB Physics HL."
model-index:
- name: IB-Physics-Mini-GPT
  results: []
---

# IB-Physics-Mini-GPT (from-scratch tiny GPT-2)

A small GPT-2–style casual LLM trained from scratch on a compact IB Physics HL corpus,
then lightly instruction-tuned for short Q&A. Purpose: show end-to-end skill
(tokenizer → pretrain → SFT → eval → deploy on a HF Space).

**Why small?** Fits student budget. **Why physics?** Narrow domain = good coverage with little data.

## Quickstart
```bash
pip install -r requirements.txt
# 1) prepare data
python train/prepare_corpus.py
python train/build_tokenizer.py
# 2) pretrain (tiny)
python train/pretrain.py
# 3) sft
python train/sft.py
# 4) sample
python train/gen_sample.py --prompt "Explain inertia in one sentence."
# 5) push to Hugging Face
python scripts/push_to_hf.py --repo your-username/ib-physics-mini-gpt
```

## Demo Space
This repo includes a Gradio app (`space_app/app.py`). Create a Hugging Face Space,
point it at this folder, set Space SDK=Gradio, Python backend.

## Notes
- Educational demo; not for safety-critical use.
- Inspired by classic GPT papers and hands-on books/videos.