Instructions to use sergioalves/7b2f350b-655d-4b14-a9cf-b02a524b10f6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sergioalves/7b2f350b-655d-4b14-a9cf-b02a524b10f6 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B") model = PeftModel.from_pretrained(base_model, "sergioalves/7b2f350b-655d-4b14-a9cf-b02a524b10f6") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from sergioalves/7b2f350b-655d-4b14-a9cf-b02a524b10f6: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/sergioalves/7b2f350b-655d-4b14-a9cf-b02a524b10f6/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://sergioalves/7b2f350b-655d-4b14-a9cf-b02a524b10f6/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/sergioalves/7b2f350b-655d-4b14-a9cf-b02a524b10f6/resolve/main/adapter_model.safetensors
336 MB
- Xet hash:
- c814d2fa457f204480f615c24c31372e7c805e6e5b0da260c8253fbe66fb9d18
- Size of remote file:
- 336 MB
- SHA256:
- 223c5bd901e4b771ee9e8fbe981ae71265d4e119b216ae17cd26e8f925688775
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.