Instructions to use Cshavi/bespoke_ft_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cshavi/bespoke_ft_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Cshavi/bespoke_ft_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Cshavi/bespoke_ft_model: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Cshavi/bespoke_ft_model/resolve/main/tokenizer.json
- Command line
-
hf download hf://Cshavi/bespoke_ft_model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Cshavi/bespoke_ft_model/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 48880d1e21846167f340840a04a46385030d1332684a1e0ae39455ba50d886c2
- Size of remote file:
- 11.4 MB
- SHA256:
- 14552ed91ab21b6a1a78c89c4fb8f74658e0b0a2aa766fb79fc9ce8780501742
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.