Instructions to use Shengkun/llama2-mistral-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Shengkun/llama2-mistral-instruct with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "Shengkun/llama2-mistral-instruct") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8c39d8b163b4e7939e625386894eb96b6a2c745148cfba620245e988673a9e0
- Size of remote file:
- 80.1 MB
- SHA256:
- 48e2155babec93cb1facabeb55b473c2af9a9d2e82c36fac2be25d31ecfbc56d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.