Instructions to use matteodagos/MNLP_M2_mcqa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
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, )
- Xet hash:
- c796ba9a994a452c759f61542ba476691e61a0f54cfc28f81618b5e43894c7d2
- Size of remote file:
- 1.19 GB
- SHA256:
- 92ff99a4c4704d552ce05aa3628741528be95e28167788c082f9b3344b117904
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.