Instructions to use socius/Llama-Centaur-8B-LoRA-r4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use socius/Llama-Centaur-8B-LoRA-r4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "socius/Llama-Centaur-8B-LoRA-r4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
|
Download README.md from socius/Llama-Centaur-8B-LoRA-r4: direct link, hf CLI and curl.
- Browser
- Download file 5.61 kB
-
https://huggingface.co/socius/Llama-Centaur-8B-LoRA-r4/resolve/main/README.md
- Command line
-
hf download hf://socius/Llama-Centaur-8B-LoRA-r4/README.md
-
curl -L -o README.md https://huggingface.co/socius/Llama-Centaur-8B-LoRA-r4/resolve/main/README.md
5.61 kB
metadata
license: llama3.1
datasets:
- marcelbinz/Psych-101
language:
- en
base_model:
- unsloth/Llama-3.1-8B
base_model_relation: adapter
pipeline_tag: text-generation
library_name: peft
tags:
- psychology
- cognitive science
- human behavior
- unsloth
- lora
Llama-Centaur-8B-LoRA-r4
LoRA adapter for Llama-Centaur-8B, 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/Llama-3.1-8B |
| 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/Llama-3.1-8B, or evaluate with the project's
eval_model.py --backend unsloth.