Instructions to use CodCodingCode/Llama3-OpenBioLLM-8B-4bit-peft-adapter-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CodCodingCode/Llama3-OpenBioLLM-8B-4bit-peft-adapter-test with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("aaditya/Llama3-OpenBioLLM-8B") model = PeftModel.from_pretrained(base_model, "CodCodingCode/Llama3-OpenBioLLM-8B-4bit-peft-adapter-test") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from CodCodingCode/Llama3-OpenBioLLM-8B-4bit-peft-adapter-test: direct link, hf CLI and curl.
- Browser
- Download file 54.6 MB
-
https://huggingface.co/CodCodingCode/Llama3-OpenBioLLM-8B-4bit-peft-adapter-test/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://CodCodingCode/Llama3-OpenBioLLM-8B-4bit-peft-adapter-test/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/CodCodingCode/Llama3-OpenBioLLM-8B-4bit-peft-adapter-test/resolve/main/adapter_model.safetensors
54.6 MB
- Xet hash:
- 57da5cddbaa51d191beef91cbdf44da0184ae32db01f2d69ae3c99e74f34b2d1
- Size of remote file:
- 54.6 MB
- SHA256:
- 5e93d8597cf8c79dc60dacc4acbef28dcc865a254964d0879019de4d480bec1d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.