Instructions to use socius/Smoltaur-0.4B-LoRA-r64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use socius/Smoltaur-0.4B-LoRA-r64 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-360M") model = PeftModel.from_pretrained(base_model, "socius/Smoltaur-0.4B-LoRA-r64") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use socius/Smoltaur-0.4B-LoRA-r64 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 socius/Smoltaur-0.4B-LoRA-r64 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 socius/Smoltaur-0.4B-LoRA-r64 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for socius/Smoltaur-0.4B-LoRA-r64 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="socius/Smoltaur-0.4B-LoRA-r64", max_seq_length=2048, )
metadata
license: apache-2.0
datasets:
- marcelbinz/Psych-101
language:
- en
base_model:
- unsloth/SmolLM2-360M
base_model_relation: adapter
pipeline_tag: text-generation
library_name: peft
tags:
- psychology
- cognitive science
- human behavior
- unsloth
- lora
Smoltaur-0.4B-LoRA-r64
LoRA adapter for Smoltaur-0.4B, fine-tuned on the full Psych-101 as part of the LoRA-rank sweep and dataset-size ablation for Small Foundation Models of Human Cognition and Behaviour.
| field | value |
|---|---|
| base model | unsloth/SmolLM2-360M |
| LoRA rank | 64 (alpha = rank, rsLoRA) |
| data fraction | 100% of Psych-101 |
| training | 1 epoch, completion-only loss, seed 3407 |
Load with PEFT on top of unsloth/SmolLM2-360M, or evaluate with the project's
eval_model.py --backend unsloth.