Instructions to use bs-la/bloomz-7b1-4b-xp3ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bs-la/bloomz-7b1-4b-xp3ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bs-la/bloomz-7b1-4b-xp3ru")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bs-la/bloomz-7b1-4b-xp3ru") model = AutoModel.from_pretrained("bs-la/bloomz-7b1-4b-xp3ru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from bs-la/bloomz-7b1-4b-xp3ru: direct link, hf CLI and curl.
- Browser
- Download file 14.5 MB
-
https://huggingface.co/bs-la/bloomz-7b1-4b-xp3ru/resolve/main/tokenizer.json
- Command line
-
hf download hf://bs-la/bloomz-7b1-4b-xp3ru/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/bs-la/bloomz-7b1-4b-xp3ru/resolve/main/tokenizer.json
14.5 MB
- Xet hash:
- 18df21c642d0e45e4e3d7817bebac91ad71ab31006d63120dc8821fe0da91f87
- Size of remote file:
- 14.5 MB
- SHA256:
- 3fa39cd4b1500feb205bcce3b9703a4373414cafe4970e0657b413f7ddd2a9d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.