Instructions to use harris1/mistral_7b_multi_label_abstract_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harris1/mistral_7b_multi_label_abstract_classification with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("harris1/mistral_7b_multi_label_abstract_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from harris1/mistral_7b_multi_label_abstract_classification: direct link, hf CLI and curl.
- Browser
- Download file 369 MB
-
https://huggingface.co/harris1/mistral_7b_multi_label_abstract_classification/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://harris1/mistral_7b_multi_label_abstract_classification/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/harris1/mistral_7b_multi_label_abstract_classification/resolve/main/adapter_model.safetensors
369 MB
- Xet hash:
- 959f71b3872c4afe38c46b8a80a7a7add18dd2ba0ea50e4c7a1b0897660f22ba
- Size of remote file:
- 369 MB
- SHA256:
- 5d312fe6799a08cf86e6b125044be87c4446cb27a29507a0ae8e7efe812a92ab
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