Instructions to use EMBO/bio-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBO/bio-lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EMBO/bio-lm")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EMBO/bio-lm") model = AutoModelForMaskedLM.from_pretrained("EMBO/bio-lm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from EMBO/bio-lm: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/EMBO/bio-lm/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://EMBO/bio-lm/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/EMBO/bio-lm/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 074b59641f2846652178870173753faa657706cd0ae2242dde9241c245ebdc7a
- Size of remote file:
- 499 MB
- SHA256:
- d674427b55d9ba7fbebf0efc83383e28a5af890787436ec6c89f0cb6951d1155
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.