Model save
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README.md
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---
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base_model: google/gemma-3-1b-pt
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library_name: transformers
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model_name: gemma-text-to-sql
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tags:
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- generated_from_trainer
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- trl
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licence: license
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---
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# Model Card for gemma-text-to-sql
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This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="java2core/gemma-text-to-sql", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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---
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base_model: google/gemma-3-1b-pt
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library_name: transformers
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model_name: gemma-3-1b-text-to-sql
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tags:
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- generated_from_trainer
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- trl
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licence: license
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---
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# Model Card for gemma-3-1b-text-to-sql
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This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="java2core/gemma-3-1b-text-to-sql", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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