Instructions to use infgrad/puff-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use infgrad/puff-large-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="infgrad/puff-large-v1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("infgrad/puff-large-v1") model = AutoModel.from_pretrained("infgrad/puff-large-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 683f44100c2254d43b77cb6599090872cb632421f91153a1d0e5ee2b4110b872
- Size of remote file:
- 2.47 GB
- SHA256:
- 30cd755f63be9334d2dfef6d643908b49853a0e723e897c4eb31d4ff111fd8ec
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