Instructions to use venkatasg/lil-bevo-x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use venkatasg/lil-bevo-x with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="venkatasg/lil-bevo-x")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("venkatasg/lil-bevo-x") model = AutoModelForMaskedLM.from_pretrained("venkatasg/lil-bevo-x", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from venkatasg/lil-bevo-x: direct link, hf CLI and curl.
- Browser
- Download file 447 MB
-
https://huggingface.co/venkatasg/lil-bevo-x/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://venkatasg/lil-bevo-x/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/venkatasg/lil-bevo-x/resolve/main/pytorch_model.bin
447 MB
- Xet hash:
- 5b47a90df0762585d35cd3f994e68f762e6a691ed9cc297ab6cd3e13c138a087
- Size of remote file:
- 447 MB
- SHA256:
- 2897e2deec2332e2fe99c1455c55a2c42c234bf7b8811849e98d8c65f70dcaff
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