Upload lfm2-physics

README.md

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+---
+license: apache-2.0
+base_model: LiquidAI/LFM2-350M
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- physics
+- next-frame-prediction
+- lora
+- sft
+- trl
+- unsloth
+- icml-2026
+---
+
+# lfm2-physics
+
+LoRA fine-tune of `LiquidAI/LFM2-350M` for 2D rigid body physics next-frame prediction. Part of an ICML-2026 study comparing fine-tuned LMs vs. from-scratch GPTs on physics trajectory modelling.
+
+## Adapter details
+
+- **Base**: `LiquidAI/LFM2-350M`
+- **Adapter type**: LoRA, r=32, alpha=64, dropout=0.0
+- **Target modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Trainer**: `SFTTrainer` (TRL) via Unsloth
+- **Curriculum**: 5 stages of increasing scene complexity
+- **Task**: autoregressive next-frame prediction over 200-frame rigid-body scenes
+
+## Stages
+
+- `stage0/` ... `stage4/` — checkpoints from each curriculum stage
+- `final/` — final adapter after all stages
+
+Each stage directory contains an Unsloth-saved adapter (`adapter_config.json`, `adapter_model.safetensors`, tokenizer files).
+
+## Usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2-350M")
+model = PeftModel.from_pretrained(base, "AlexWortega/lfm2-physics", subfolder="final")
+tokenizer = AutoTokenizer.from_pretrained("AlexWortega/lfm2-physics", subfolder="final")
+```
+
+## Training data
+
+Trained on ~900K scenes across 24 "seen" scenario types. See [physics-scenarios-packed](https://huggingface.co/datasets/AlexWortega/physics-scenarios-packed).
+
+## Citation
+
+ICML-2026 submission (in progress).