Instructions to use EdBerg/continue01_arabic_trained_model_mistral7b_completion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use EdBerg/continue01_arabic_trained_model_mistral7b_completion with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-v0.3-bnb-4bit") model = PeftModel.from_pretrained(base_model, "EdBerg/continue01_arabic_trained_model_mistral7b_completion") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download training_args.bin from EdBerg/continue01_arabic_trained_model_mistral7b_completion: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/EdBerg/continue01_arabic_trained_model_mistral7b_completion/resolve/main/training_args.bin
- Command line
-
hf download hf://EdBerg/continue01_arabic_trained_model_mistral7b_completion/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/EdBerg/continue01_arabic_trained_model_mistral7b_completion/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- c0add8a857df73d06660473c55fe7f4da1448f2b85e257be119fa888b07f8fe8
- Size of remote file:
- 5.3 kB
- SHA256:
- 599bf43277ad02d0e5de7c9f4ca988b79be8ca8315278b12fc46d9f4c5d73e04
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.