Instructions to use AhmedLet/Qwen_0.5_python_codes_mbpp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AhmedLet/Qwen_0.5_python_codes_mbpp with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AhmedLet/Qwen_0.5_python_codes_mbpp", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use AhmedLet/Qwen_0.5_python_codes_mbpp 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 AhmedLet/Qwen_0.5_python_codes_mbpp 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 AhmedLet/Qwen_0.5_python_codes_mbpp to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AhmedLet/Qwen_0.5_python_codes_mbpp to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="AhmedLet/Qwen_0.5_python_codes_mbpp", max_seq_length=2048, )
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base_model:
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tags:
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license: apache-2.0
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- **License:** apache-2.0
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---
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base_model:
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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license: apache-2.0
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language:
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datasets:
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- google-research-datasets/mbpp
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- mlabonne/FineTome-100k
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- MohamedSaeed-dev/python_dataset_codes
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---
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# Uploaded model
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- **Developed by:** AhmedLet
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- **License:** apache-2.0
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