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
- Xet hash:
- 51d593ef53d5cb22dfed589b5d6ff8a23e2e32f9b640d9830db27aa0efd7b4e8
- Size of remote file:
- 17.2 MB
- SHA256:
- 0968dcc0ee8e56c7dccd34a7f51f8065ea0cb9e2cc529e3243d1e5c0a4bdaa0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.