kristiannordby/llama3-sqlcoder-ft

Model Card for Model ID

This text-to-sql model was finetuned on army-aviation-specific data.

Model Details

To load the model:

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kristiannordby/llama3-sqlcoder-ft")
model = AutoModelForCausalLM.from_pretrained("kristiannordby/llama3-sqlcoder-ft")

Prompt:

### Task
Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION]

### Database Schema
The query will run on a database with the following schema:
{table_metadata_string_DDL_statements}

### Answer
Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION]
[SQL]

To prompt the model for generation:

def build_prompt(user_question, create_table_statements):
    return (
        "### Task\n"
        f"Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION]\n\n"
        "### Database Schema\n"
        "The query will run on a database with the following schema:\n"
        f"{create_table_statements}\n\n"
        "### Answer\n"
        f"Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION]\n"
        "[SQL]\n"
    )

def build_output(sql):
    # Add a newline at end; if the data has a closing "[/SQL]", add it here!
    return f"{sql.strip()}\n"

create_table_statements = "YOUR TABLE SCHEMA HERE"

def sqllamma(question):
    input_ids = tokenizer(build_prompt(question, create_table_statements), return_tensors="pt", padding = True, truncation = True, max_length = 512).input_ids.to(model.device)
    outputs = model.generate(input_ids, max_new_tokens=100)
    output = tokenizer.decode(outputs[0])
    sql = output.split("###")[3].split("[SQL]")[1].strip()    
    return sql

sqllama("YOUR QUESTION HERE")

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: @kristiannordby
• Funded by [optional]: AI2C, USMA D/MATH
• Shared by [optional]: [More Information Needed]
• Model type: [Text-to-SQL]
• Language(s) (NLP): English, SQL
• License: [More Information Needed]
• Finetuned from model [optional]: Defog/sqlcoder-7b-2

Model Sources [optional]

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• Demo [optional]: [More Information Needed]

Uses

This model was finetuned on an Army Aviation Question-SQL dataset.

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Evaluation

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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