Model save
Browse files- README.md +58 -0
- adapter_config.json +3 -3
- adapter_model.safetensors +2 -2
- runs/Mar22_17-56-18_HAL/events.out.tfevents.1742684196.HAL.17165.0 +3 -0
- runs/Mar22_17-57-48_HAL/events.out.tfevents.1742684290.HAL.17315.0 +3 -0
- runs/Mar22_18-00-53_HAL/events.out.tfevents.1742684618.HAL.17556.0 +3 -0
- runs/Mar22_18-04-24_HAL/events.out.tfevents.1742684733.HAL.17866.0 +3 -0
- training_args.bin +1 -1
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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- sft
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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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## Quick start
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```python
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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="kylemite/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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## Training procedure
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.15.2
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- Transformers: 4.50.0.dev0
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- Pytorch: 2.6.0
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- Datasets: 3.3.2
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- Tokenizers: 0.21.1
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## Citations
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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adapter_config.json
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha":
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"lora_bias": false,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"embed_tokens"
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],
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"peft_type": "LORA",
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"r":
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"gate_proj",
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"up_proj"
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],
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"task_type": "CAUSAL_LM",
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 8,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"embed_tokens"
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],
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"peft_type": "LORA",
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"r": 8,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"gate_proj",
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"down_proj",
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"up_proj"
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],
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"task_type": "CAUSAL_LM",
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adapter_model.safetensors
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