---
license: other
library_name: transformers
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-2b
model-index:
- name: gemma-2b_text_to_sql
  results: []
inference:
  parameters:
    do_sample: false
    max_length: 200
widget:
- text: >-
    CREATE TABLE stadium (
        stadium_id number,
        location text,
        name text,
        capacity number,
    )


    -- Using valid SQLite, answer the following questions for the tables
    provided above.


    -- how many stadiums in total?


    SELECT
  example_title: Number stadiums
- text: >-
    CREATE TABLE work_orders ( ID NUMBER, CREATED_AT TEXT, COST FLOAT,
    INVOICE_AMOUNT FLOAT, IS_DUE BOOLEAN, IS_OPEN BOOLEAN, IS_OVERDUE BOOLEAN,
    COUNTRY_NAME TEXT, )


    -- Using valid SQLite, answer the following questions for the tables
    provided above.


    -- how many work orders are open?


    SELECT
  example_title: Open work orders
- text: >-
    CREATE TABLE stadium ( stadium_id number, location text, name text, capacity
    number, highest number, lowest number, average number )


    CREATE TABLE singer ( singer_id number, name text, country text, song_name
    text, song_release_year text, age number, is_male others )


    CREATE TABLE concert ( concert_id number, concert_name text, theme text,
    stadium_id text, year text )


    CREATE TABLE singer_in_concert ( concert_id number, singer_id text )


    -- Using valid SQLite, answer the following questions for the tables
    provided above.


    -- What is the maximum, the average, and the minimum capacity of stadiums ?


    SELECT
  example_title: Stadium capacity
pipeline_tag: text2text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# text_to_sql

This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 25
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- PEFT 0.8.2
- Transformers 4.38.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2