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:
# pip install -U transformers accelerate # 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/d86afcc7bab030c19da916f4f25ce7198ee275a0/adapter_model.safetensors
- Command line
-
hf download hf://harris1/mistral_7b_multi_label_abstract_classification@d86afcc7bab030c19da916f4f25ce7198ee275a0/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/harris1/mistral_7b_multi_label_abstract_classification/resolve/d86afcc7bab030c19da916f4f25ce7198ee275a0/adapter_model.safetensors
369 MB
- Xet hash:
- d784b0e689eaeeebd03700ccbe8019bcb88274431e44615a2c3314ac07e81688
- Size of remote file:
- 369 MB
- SHA256:
- d46f821d2d399a2722984a071d53d14ec1316ac66ead38d3a366283e88e017ee
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