Instructions to use allenai/specter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/specter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="allenai/specter")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allenai/specter") model = AutoModel.from_pretrained("allenai/specter", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from allenai/specter: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/allenai/specter/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://allenai/specter/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/allenai/specter/resolve/main/flax_model.msgpack
440 MB
- Xet hash:
- a0f6e5cafa9ee224b21c69e2a48a80f6a4a8b68b3af827a69a0164cb44f4dad8
- Size of remote file:
- 440 MB
- SHA256:
- 6d84dc0bb064c651956611b39ed13a043d88ee7a1dd1239913ac06840a7fa53c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.