Instructions to use osiria/blaze-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osiria/blaze-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="osiria/blaze-it")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("osiria/blaze-it") model = AutoModelForMaskedLM.from_pretrained("osiria/blaze-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from osiria/blaze-it: direct link, hf CLI and curl.
- Browser
- Download file 217 MB
-
https://huggingface.co/osiria/blaze-it/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://osiria/blaze-it/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/osiria/blaze-it/resolve/main/pytorch_model.bin
217 MB
- Xet hash:
- 95b029df20010684c4d12148e10e0a628b2b920a3c8eeca6e61a4ef33ffe8533
- Size of remote file:
- 217 MB
- SHA256:
- cb6a570f5f7fc9adef89546c752f6bef576958c55f38839827ebc99e3cb43c23
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.