Instructions to use devMubashir/gemma-3-text-to-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devMubashir/gemma-3-text-to-sql with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("devMubashir/gemma-3-text-to-sql", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -51337,7 +51337,7 @@
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| 51337 |
"image_token": "<image_soft_token>",
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| 51338 |
"model_max_length": 32768,
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| 51339 |
"pad_token": "<pad>",
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| 51340 |
-
"padding_side": "
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| 51341 |
"processor_class": "Gemma3Processor",
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| 51342 |
"sp_model_kwargs": null,
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| 51343 |
"spaces_between_special_tokens": false,
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| 51337 |
"image_token": "<image_soft_token>",
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| 51338 |
"model_max_length": 32768,
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| 51339 |
"pad_token": "<pad>",
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| 51340 |
+
"padding_side": "left",
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| 51341 |
"processor_class": "Gemma3Processor",
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| 51342 |
"sp_model_kwargs": null,
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| 51343 |
"spaces_between_special_tokens": false,
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