Upload lfm2-scenarios

README.md

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+---
+license: apache-2.0
+base_model: LiquidAI/LFM2-350M
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- physics
+- scenarios
+- next-frame-prediction
+- lora
+- sft
+- trl
+- unsloth
+- icml-2026
+---
+
+# lfm2-scenarios
+
+Sister checkpoint to [lfm2-physics](https://huggingface.co/AlexWortega/lfm2-physics) — LoRA fine-tune of `LiquidAI/LFM2-350M` on the physics scenarios dataset, with a different training regime / curriculum sampling.
+
+## 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, includes scenario-type stratified sampling
+- **Task**: autoregressive next-frame prediction; conditioning includes scenario Type, Difficulty, Static geometry, Constraints
+
+## Stages
+
+- `stage0/` ... `stage4/` — checkpoints from each curriculum stage
+- `final/` — final adapter
+
+## 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-scenarios", subfolder="final")
+tokenizer = AutoTokenizer.from_pretrained("AlexWortega/lfm2-scenarios", subfolder="final")
+```
+
+## Training data
+
+900K scenes, 24 seen scenario types (avalanche, basketball, billiards, breakout, bridge, chain, conveyor, dominos, explosion, funnel, head_on, jenga, marble_run, orbit, pendulum, pinball, plinko, projectile, pyramid, seesaw, ski_jump, tower, wind, wrecking_ball). 6 types held out for OOD eval (pong, bowling, ramp_roll, angry_birds, hourglass, newtons_cradle).
+
+## Citation
+
+ICML-2026 submission (in progress).