Instructions to use kamel-usp/jbcs2025_llama31_8b-balanced-C1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kamel-usp/jbcs2025_llama31_8b-balanced-C1 with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "kamel-usp/jbcs2025_llama31_8b-balanced-C1") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from kamel-usp/jbcs2025_llama31_8b-balanced-C1: direct link, hf CLI and curl.
- Browser
- Download file 84 MB
-
https://huggingface.co/kamel-usp/jbcs2025_llama31_8b-balanced-C1/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://kamel-usp/jbcs2025_llama31_8b-balanced-C1/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/kamel-usp/jbcs2025_llama31_8b-balanced-C1/resolve/main/adapter_model.safetensors
84 MB
- Xet hash:
- 4f6ed7da12137b659fcc1c38a05823b6bd4438c8d3be8641d9c91392a9a09505
- Size of remote file:
- 84 MB
- SHA256:
- b6397979039118a3634b35deb1598ac9506c0cc7ceca748624eca7a92dbeec49
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.