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/532d5f0bcd1fa92ee91951adc33844bf3d51bd68/adapter_model.safetensors
- Command line
-
hf download hf://harris1/mistral_7b_multi_label_abstract_classification@532d5f0bcd1fa92ee91951adc33844bf3d51bd68/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/harris1/mistral_7b_multi_label_abstract_classification/resolve/532d5f0bcd1fa92ee91951adc33844bf3d51bd68/adapter_model.safetensors
369 MB
- Xet hash:
- c13d56c8b51a46fc416b851cd57ea442d8cc4b946721567acab0ff35ab69af5f
- Size of remote file:
- 369 MB
- SHA256:
- e00f94a09f5cc0ed33c953a851b2695376ee5c2e914adf7ed5dc49dca5299319
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.