Instructions to use Majipa/text-to-SQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Majipa/text-to-SQL with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Majipa/text-to-SQL", dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Majipa/text-to-SQL 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 Majipa/text-to-SQL 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 Majipa/text-to-SQL to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Majipa/text-to-SQL to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Majipa/text-to-SQL", max_seq_length=2048, )
- Xet hash:
- 4b7e1f225f021212d757329d0bcb52ed90cd23f0546c3389428b6d7feb23f38f
- Size of remote file:
- 239 MB
- SHA256:
- 0060321514b97618a8278a5eaf23056abcffae5285e364926070212ec484a2fd
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