Instructions to use fadliaulawi/med42-v2-Reflect-PubMedQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fadliaulawi/med42-v2-Reflect-PubMedQA with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fadliaulawi/med42-v2-Reflect-PubMedQA", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use fadliaulawi/med42-v2-Reflect-PubMedQA 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 fadliaulawi/med42-v2-Reflect-PubMedQA 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 fadliaulawi/med42-v2-Reflect-PubMedQA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for fadliaulawi/med42-v2-Reflect-PubMedQA to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="fadliaulawi/med42-v2-Reflect-PubMedQA", max_seq_length=2048, )
- Xet hash:
- 4afbc9a134fa30137c19fc48195c6be398c849eb9e757c2974e06f2f4df26990
- Size of remote file:
- 168 MB
- SHA256:
- 82dc65903b1cac60c16acb3354851ffe159592b80ff7803d1f2d8976a8f5d0f8
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