Upload gpt-physics

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: pytorch
+tags:
+- physics
+- next-frame-prediction
+- gpt
+- mup
+- rigid-body-dynamics
+- icml-2026
+---
+
+# gpt-physics
+
+A small GPT trained from scratch to predict 2D rigid body physics trajectories. Part of an ICML-2026 study on whether language models can learn physical dynamics from text-encoded simulation data.
+
+## Model details
+
+- **Architecture**: 6-layer GPT, learned positional embeddings, tied LM head
+- **Tokenizer**: digit-level `PhysicsTokenizer` (custom)
+- **Scaling**: muP for hyperparameter transfer
+- **Training**: curriculum learning over 5 difficulty stages
+- **Task**: autoregressive next-frame prediction over 200-frame rigid-body scenes
+- **Domain**: 2D rigid body dynamics simulated with Pymunk / Chipmunk2D
+
+## Files
+
+- `best_model.pt` — best validation checkpoint (~69 MB)
+- `checkpoint_latest.pt` — latest training step (~158 MB)
+- `checkpoint_epoch0_step500.pt` — early checkpoint (~158 MB)
+
+State dicts contain raw `transformer.*` and `lm_head.*` keys for a stock 6-layer GPT — load with the project's `src/scratch/gpt.py` model class.
+
+## Training data
+
+Trained on ~900K scenes across 24 "seen" scenario types (collisions, stacking, ramps, constraints, mini-games, complex). See [physics-scenarios-packed](https://huggingface.co/datasets/AlexWortega/physics-scenarios-packed) and [physics-scenarios-raw](https://huggingface.co/datasets/AlexWortega/physics-scenarios-raw).
+
+## Intended use
+
+Research on whether autoregressive LMs can internalize physical dynamics. Not intended for production physics simulation — use Pymunk for that.
+
+## Citation
+
+ICML-2026 submission (in progress).

best_model.pt

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checkpoint_epoch0_step500.pt

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checkpoint_latest.pt

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