Instructions to use allegro/herbert-large-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allegro/herbert-large-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="allegro/herbert-large-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-large-cased") model = AutoModel.from_pretrained("allegro/herbert-large-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from allegro/herbert-large-cased: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/allegro/herbert-large-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://allegro/herbert-large-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/allegro/herbert-large-cased/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- fa1fe7a8f85bab8b6386bcabb804ffe8a1df8cb103f2b149e3584e6d5b6df423
- Size of remote file:
- 1.42 GB
- SHA256:
- 66bacb3455481bd3fdd4d125abba6da580625fcbfe933115005a462742b8d369
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