Instructions to use ebtesal/shawgpt-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ebtesal/shawgpt-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, "ebtesal/shawgpt-ft") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from ebtesal/shawgpt-ft: direct link, hf CLI and curl.
- Browser
- Download file 4.28 MB
-
https://huggingface.co/ebtesal/shawgpt-ft/resolve/main/optimizer.pt
- Command line
-
hf download hf://ebtesal/shawgpt-ft/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ebtesal/shawgpt-ft/resolve/main/optimizer.pt
4.28 MB
- Xet hash:
- fdc50a5fb5538d3c51bb74274979907bb8e7fe33d4cd07c86a9d0e1d5922e298
- Size of remote file:
- 4.28 MB
- SHA256:
- 35b1e2346458f1817007ae59db6a742ece72f561e7d61ff3720c0d2265faed34
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.