Instructions to use Cem13/mistral_instruct_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Cem13/mistral_instruct_generation with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "Cem13/mistral_instruct_generation") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Cem13/mistral_instruct_generation: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/Cem13/mistral_instruct_generation/resolve/main/training_args.bin
- Command line
-
hf download hf://Cem13/mistral_instruct_generation/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Cem13/mistral_instruct_generation/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- 3f9e798da6e7e9225c14c8a3326fe1a927deab439e72dcc2c3f480f6072ae7c6
- Size of remote file:
- 5.05 kB
- SHA256:
- 429ffc72cf8a2e9794f479a3ee523ab775d1474d15df35a4be71eddd76c023c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.