Instructions to use youxiazhao/Binary_classification_Qwen2.5-7B-Instruct_500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use youxiazhao/Binary_classification_Qwen2.5-7B-Instruct_500 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("youxiazhao/Binary_classification_Qwen2.5-7B-Instruct_500", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use youxiazhao/Binary_classification_Qwen2.5-7B-Instruct_500 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 youxiazhao/Binary_classification_Qwen2.5-7B-Instruct_500 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 youxiazhao/Binary_classification_Qwen2.5-7B-Instruct_500 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for youxiazhao/Binary_classification_Qwen2.5-7B-Instruct_500 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="youxiazhao/Binary_classification_Qwen2.5-7B-Instruct_500", max_seq_length=2048, )
- Xet hash:
- 63f389595c9f078e259160a2b58f7d0f4060366badf495a1616f0077a38385ed
- Size of remote file:
- 162 MB
- SHA256:
- ebad5728a828a72a2b3d97ac357975003367ad3a56782eaa2303c84cbf5eab0d
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