Instructions to use aloxatel/W2L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloxatel/W2L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aloxatel/W2L")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aloxatel/W2L") model = AutoModelForSequenceClassification.from_pretrained("aloxatel/W2L", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from aloxatel/W2L: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/aloxatel/W2L/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://aloxatel/W2L/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/aloxatel/W2L/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- 2f915c0068f3c4355d058389d26fa7f9adcae3c59dd654fe0caabd6f832167d2
- Size of remote file:
- 1.42 GB
- SHA256:
- 4ebbdf82df2a0e097e51f1d93cd809505b855fbce0159ed25628417c2b8620bd
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