Instructions to use uracoder/llama-8b-jarvis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use uracoder/llama-8b-jarvis with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("uracoder/llama-8b-jarvis", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from uracoder/llama-8b-jarvis: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/uracoder/llama-8b-jarvis/resolve/main/tokenizer.json
- Command line
-
hf download hf://uracoder/llama-8b-jarvis/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/uracoder/llama-8b-jarvis/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- f50a225d9f53b6d10de736023ad24fa9ce92634801004eb39d881f415a4784cb
- Size of remote file:
- 17.2 MB
- SHA256:
- 3c5cf44023714fb39b05e71e425f8d7b92805ff73f7988b083b8c87f0bf87393
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.