How to use from
Unsloth StudioInstall Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AlexWortega/lfm2-physics to start chattingUsing HuggingFace Spaces for Unsloth
# No setup required# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AlexWortega/lfm2-physics to start chattingLoad model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="AlexWortega/lfm2-physics",
max_seq_length=2048,
)Quick Links
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 stagefinal/— final adapter after all stages
Each stage directory contains an Unsloth-saved adapter (adapter_config.json, adapter_model.safetensors, tokenizer files).
Usage
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.
Citation
ICML-2026 submission (in progress).
- Downloads last month
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Model tree for AlexWortega/lfm2-physics
Base model
LiquidAI/LFM2-350M
Install Unsloth Studio (macOS, Linux, WSL)
# Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for AlexWortega/lfm2-physics to start chatting