Instructions to use AravD/Paul_AI-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AravD/Paul_AI-ft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.2-GPTQ") model = PeftModel.from_pretrained(base_model, "AravD/Paul_AI-ft") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AravD/Paul_AI-ft: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/AravD/Paul_AI-ft/resolve/main/training_args.bin
- Command line
-
hf download hf://AravD/Paul_AI-ft/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AravD/Paul_AI-ft/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- a271c8dee01b650ea5b03a1d1baa5e862b557f050deee4edd0f96f9be3437880
- Size of remote file:
- 5.18 kB
- SHA256:
- d8de0db15811494cecb137da51158fd49b421e81214775e8b8179caa91238fed
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.