Instructions to use AlessaC/qwen2p5b_em_badmed_r32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlessaC/qwen2p5b_em_badmed_r32 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AlessaC/qwen2p5b_em_badmed_r32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use AlessaC/qwen2p5b_em_badmed_r32 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 AlessaC/qwen2p5b_em_badmed_r32 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 AlessaC/qwen2p5b_em_badmed_r32 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AlessaC/qwen2p5b_em_badmed_r32 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="AlessaC/qwen2p5b_em_badmed_r32", max_seq_length=2048, )
- Xet hash:
- c404032cbae74e4494c66ed25e5b37a87f93d7d8cf170b5a41dd8a671c9af9ce
- Size of remote file:
- 70.4 MB
- SHA256:
- eda740341472735f10d371977436dca3ffb1fa62803d1b66ccc55b4f92c1a578
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