Text Generation
PEFT
Safetensors
physics
scenarios
next-frame-prediction
lora
sft
trl
unsloth
icml-2026
Instructions to use AlexWortega/lfm2-scenarios with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AlexWortega/lfm2-scenarios with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use AlexWortega/lfm2-scenarios with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for AlexWortega/lfm2-scenarios to start chatting
Install 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-scenarios to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AlexWortega/lfm2-scenarios to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="AlexWortega/lfm2-scenarios", max_seq_length=2048, )
File size: 922 Bytes
5ebc41b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"stage": 3,
"timestamp": "2026-02-07T02:11:38.871641",
"config": {
"data_dir": "/home/alexw/data_scenarios/train",
"output_dir": "/home/alexw/checkpoints/lfm2-scenarios",
"epochs_per_stage": 1,
"batch_size": 4,
"grad_accum": 8,
"lr": 0.0002,
"max_seq_length": 8192,
"curriculum_stages": 5,
"max_examples_per_stage": 50000,
"max_context_frames": 200,
"complexity_metric": "difficulty",
"physics_loss_weight": 0.01,
"resume": null,
"wandb_project": "physics-llm",
"wandb_offline": true,
"lora_r": 32,
"lora_alpha": 64,
"model": "LiquidAI/LFM2-350M",
"timestamp": "2026-02-05T15:22:28.882113"
},
"metrics": {
"train_runtime": 24868.7124,
"train_samples_per_second": 2.011,
"train_steps_per_second": 0.063,
"total_flos": 6.976691242146017e+17,
"train_loss": 0.6747274479649422,
"epoch": 1.0,
"step": 1563
}
} |