Instructions to use fliarbi/mistral-hummanize1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fliarbi/mistral-hummanize1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "fliarbi/mistral-hummanize1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d550c15970720a0d4c3c55958d0b8d0016c8ec4e5d4fd8cfc9e541ac1b39a34
- Size of remote file:
- 4.86 kB
- SHA256:
- 395af09bb5f978ac9817b6e3be7c77ff7b28e7032dc799219084d5d8c0b7c665
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.