Instructions to use aydippy/dippy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aydippy/dippy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aydippy/dippy")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aydippy/dippy") model = AutoModelForSequenceClassification.from_pretrained("aydippy/dippy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from aydippy/dippy: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/aydippy/dippy/resolve/main/tf_model.h5
- Command line
-
hf download hf://aydippy/dippy/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/aydippy/dippy/resolve/main/tf_model.h5
268 MB
- Xet hash:
- f58d92883829fe04a30da0311b347f279e79a0e3ad847fb6a7f0c909fa21c206
- Size of remote file:
- 268 MB
- SHA256:
- 1f651b5961a1b91f59b5fde3325c19c84d85f13fa455c6ae84e4b857514e247c
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