Instructions to use vdos/3991d426-2cc3-404f-926d-b1e07ab81715 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vdos/3991d426-2cc3-404f-926d-b1e07ab81715 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Intel/neural-chat-7b-v3-3") model = PeftModel.from_pretrained(base_model, "vdos/3991d426-2cc3-404f-926d-b1e07ab81715") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from vdos/3991d426-2cc3-404f-926d-b1e07ab81715: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/vdos/3991d426-2cc3-404f-926d-b1e07ab81715/resolve/main/training_args.bin
- Command line
-
hf download hf://vdos/3991d426-2cc3-404f-926d-b1e07ab81715/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vdos/3991d426-2cc3-404f-926d-b1e07ab81715/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- f3eb48b06434c5f6fd7b56873d1692a05b13589b1e3bb761cd5ac96603d5d413
- Size of remote file:
- 6.78 kB
- SHA256:
- d6b0d72fff36805ccab56bb637b17bbba2c158bc70a36bc7fb79cb9540aa2f8c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.