Instructions to use shivam9980/mistral-news-7B-cnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shivam9980/mistral-news-7B-cnn with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shivam9980/mistral-news-7B-cnn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use shivam9980/mistral-news-7B-cnn with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for shivam9980/mistral-news-7B-cnn to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for shivam9980/mistral-news-7B-cnn to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for shivam9980/mistral-news-7B-cnn to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="shivam9980/mistral-news-7B-cnn", max_seq_length=2048, )
- Xet hash:
- af7e1c3aa4c33e5f2eab1452dd10aded0e62b39d3e3d13008c20e36fb030e99a
- Size of remote file:
- 336 MB
- SHA256:
- eb4e17674336ee210f9c4760c3a3b8b93837f62582a5e5fb41d9503416b68640
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