Instructions to use minhchuxuan/llama-2.7b-dolly-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhchuxuan/llama-2.7b-dolly-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/LMOps/minillm/checkpoints/Sheared-LLaMA-2.7B-Pruned/") model = PeftModel.from_pretrained(base_model, "minhchuxuan/llama-2.7b-dolly-lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe1774353306240c84f163fd6b812d31fa0c2c6f4adf888e667d93d4bc3a55b4
- Size of remote file:
- 10.5 MB
- SHA256:
- 61a0432fb286b22e01d824a486a6cbe8ba3364a59f630914686ca8661c2d9564
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.