Instructions to use matteodagos/MNLP_M2_mcqa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use matteodagos/MNLP_M2_mcqa_model 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 matteodagos/MNLP_M2_mcqa_model 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 matteodagos/MNLP_M2_mcqa_model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for matteodagos/MNLP_M2_mcqa_model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="matteodagos/MNLP_M2_mcqa_model", max_seq_length=2048, )
Upload tokenizer
Browse files- tokenizer.json +2 -2
tokenizer.json
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