Instructions to use Ashima/gemma-text-to-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ashima/gemma-text-to-sql with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ashima/gemma-text-to-sql", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- .gitattributes +1 -0
- README.md +58 -0
- adapter_config.json +48 -0
- adapter_model.safetensors +3 -0
- chat_template.jinja +4 -0
- runs/May02_19-03-15_349267ec95ba/events.out.tfevents.1777748595.349267ec95ba.8272.0 +3 -0
- runs/May02_19-26-07_349267ec95ba/events.out.tfevents.1777749967.349267ec95ba.16269.0 +3 -0
- runs/May02_19-32-03_349267ec95ba/events.out.tfevents.1777750323.349267ec95ba.16269.1 +3 -0
- runs/May02_19-38-28_349267ec95ba/events.out.tfevents.1777750708.349267ec95ba.16269.2 +3 -0
- runs/May02_19-42-54_349267ec95ba/events.out.tfevents.1777750974.349267ec95ba.16269.3 +3 -0
- runs/May02_20-05-12_349267ec95ba/events.out.tfevents.1777752312.349267ec95ba.26775.0 +3 -0
- tokenizer.json +3 -0
- tokenizer_config.json +19 -0
- training_args.bin +3 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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base_model: google/gemma-2-2b-it
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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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- sft
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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-2-2b-it](https://huggingface.co/google/gemma-2-2b-it).
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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="Ashima/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: 1.3.0
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- Transformers: 5.0.0
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- Pytorch: 2.10.0+cu128
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- Datasets: 4.8.5
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- Tokenizers: 0.22.2
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## Citations
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Cite TRL as:
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```bibtex
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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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```
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adapter_config.json
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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": null,
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"base_model_name_or_path": "google/gemma-2-2b-it",
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"bias": "none",
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"corda_config": null,
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| 9 |
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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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": 16,
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"lora_bias": false,
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"lora_dropout": 0.05,
|
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"lora_ga_config": null,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.19.1",
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"qalora_group_size": 16,
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"k_proj",
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"o_proj",
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"gate_proj",
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"v_proj",
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"down_proj",
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"q_proj",
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"up_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_bdlora": null,
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"use_dora": false,
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| 46 |
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"use_qalora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 83115256
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chat_template.jinja
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{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
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' + message['content'] | trim + '<end_of_turn>
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' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
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'}}{% endif %}
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runs/May02_19-03-15_349267ec95ba/events.out.tfevents.1777748595.349267ec95ba.8272.0
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version https://git-lfs.github.com/spec/v1
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runs/May02_20-05-12_349267ec95ba/events.out.tfevents.1777752312.349267ec95ba.26775.0
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<bos>",
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| 4 |
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"clean_up_tokenization_spaces": false,
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| 5 |
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"eos_token": "<eos>",
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| 6 |
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"extra_special_tokens": [
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"<start_of_turn>",
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"<end_of_turn>"
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],
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"is_local": false,
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| 11 |
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"mask_token": "<mask>",
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"model_max_length": 1000000000000000019884624838656,
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| 13 |
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"pad_token": "<pad>",
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| 14 |
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"sp_model_kwargs": {},
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| 15 |
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"spaces_between_special_tokens": false,
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| 16 |
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"tokenizer_class": "GemmaTokenizer",
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"unk_token": "<unk>",
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| 18 |
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"use_default_system_prompt": false
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:b4b59cec77b9108f2a6b75deedccc0426932e90cc248f5c302142cb61672f9f8
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size 5713
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