Instructions to use epanner/MNLP_M3_mcqa_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use epanner/MNLP_M3_mcqa_adapter with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("epanner/MNLP_M3_mcqa_adapter", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use epanner/MNLP_M3_mcqa_adapter 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 epanner/MNLP_M3_mcqa_adapter 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 epanner/MNLP_M3_mcqa_adapter to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for epanner/MNLP_M3_mcqa_adapter to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="epanner/MNLP_M3_mcqa_adapter", max_seq_length=2048, )
End of training
Browse files- adapter_config.json +4 -4
- adapter_model.safetensors +1 -1
- training_args.bin +1 -1
adapter_config.json
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"task_type": "CAUSAL_LM",
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adapter_model.safetensors
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training_args.bin
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