---
library_name: transformers
base_model: google/gemma-2b
model-index:
- name: singhjagpreet/gemma-2b_text_to_sql
  results: []
inference:
  parameters:
    do_sample: false
    max_length: 200
widget:
- text: >-
    Question: What is the average number of working horses of farms with greater
    than 45 total number of horses? Context: CREATE TABLE farm (Working_Horses
    INTEGER, Total_Horses INTEGER)
       
  example_title: Average horses
- text: >-
    Question:What are the names of the heads who are born outside the California
    state? Context: CREATE TABLE head (name VARCHAR, born_state VARCHAR)
  example_title: Name of Head
- text: >-
    Question: List official names of cities in descending order of population.
    Context: CREATE TABLE city (Official_Name VARCHAR, Population VARCHAR)
  example_title: Name of Cities
pipeline_tag: text-generation
metrics:
- bleu
- rouge
datasets:
- b-mc2/sql-create-context
license: gemma
language:
- en
tags:
- code
---

<!-- 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. -->

# gemma-2b_text_to_sql

This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) 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