Instructions to use wazzat/Mistral7B-0.3_text_simplification_fr_vikidia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use wazzat/Mistral7B-0.3_text_simplification_fr_vikidia with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.3") model = PeftModel.from_pretrained(base_model, "wazzat/Mistral7B-0.3_text_simplification_fr_vikidia") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from wazzat/Mistral7B-0.3_text_simplification_fr_vikidia: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/wazzat/Mistral7B-0.3_text_simplification_fr_vikidia/resolve/main/training_args.bin
- Command line
-
hf download hf://wazzat/Mistral7B-0.3_text_simplification_fr_vikidia/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/wazzat/Mistral7B-0.3_text_simplification_fr_vikidia/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 73e3081fd8856ec60b2badaee1d1e838e84256822dfcf5cd78294cc19331083b
- Size of remote file:
- 5.43 kB
- SHA256:
- ca646212e0d8b45c448c394612cac8839c776d9e8c86965f33b84cca92a0f669
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