marcelbinz/Psych-101
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How to use socius/Smoltaur-0.1B-LoRA-r4 with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-135M")
model = PeftModel.from_pretrained(base_model, "socius/Smoltaur-0.1B-LoRA-r4")How to use socius/Smoltaur-0.1B-LoRA-r4 with Unsloth Studio:
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-0.1B-LoRA-r4 to start chatting
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-0.1B-LoRA-r4 to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for socius/Smoltaur-0.1B-LoRA-r4 to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="socius/Smoltaur-0.1B-LoRA-r4",
max_seq_length=2048,
)LoRA adapter for Smoltaur-0.1B, 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/SmolLM2-135M |
| LoRA rank | 4 (alpha = rank, rsLoRA) |
| data fraction | 100% of Psych-101 |
| training | 1 epoch, completion-only loss, seed 3407 |
Load with PEFT on top of unsloth/SmolLM2-135M, or evaluate with the project's
eval_model.py --backend unsloth.