Instructions to use MISSAOUI/llama_model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MISSAOUI/llama_model_2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "MISSAOUI/llama_model_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07e0cb9c967d00a811627d71a213faff1ecfdb8c8914a50e2f8f9b294ee24189
- Size of remote file:
- 134 MB
- SHA256:
- 1c077816a67e0a03deff8d9875448569f665c9d7185c2e8d706e24d4c7bd36f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.