Instructions to use SamChen888/Mistral-RDE-Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SamChen888/Mistral-RDE-Finetuned with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-instruct-v0.3-bnb-4bit") model = PeftModel.from_pretrained(base_model, "SamChen888/Mistral-RDE-Finetuned") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download training_args.bin from SamChen888/Mistral-RDE-Finetuned: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/SamChen888/Mistral-RDE-Finetuned/resolve/main/training_args.bin
- Command line
-
hf download hf://SamChen888/Mistral-RDE-Finetuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SamChen888/Mistral-RDE-Finetuned/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 0c3431e63521763fdb6c53ab559677717d496bb685f2d8c86133122d037bc6a7
- Size of remote file:
- 5.43 kB
- SHA256:
- 0f6b5e03ab55c7eca1bec1843792810a25ab68a79d546ce327c2c6fad1fe4ce0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.