Instructions to use CouchCat/ma_mlc_v7_distil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CouchCat/ma_mlc_v7_distil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CouchCat/ma_mlc_v7_distil")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_mlc_v7_distil") model = AutoModelForSequenceClassification.from_pretrained("CouchCat/ma_mlc_v7_distil", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CouchCat/ma_mlc_v7_distil: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/CouchCat/ma_mlc_v7_distil/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://CouchCat/ma_mlc_v7_distil/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CouchCat/ma_mlc_v7_distil/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 29ec25c6c8a2d02bb5dda7fbd72fc79a038ca50fe8520eadc68ab4404154bf09
- Size of remote file:
- 268 MB
- SHA256:
- 5d3be0cc262b492918a496584fe6394702637ce4ee5a50ef99bc5f9820560a47
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