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

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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

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Uses

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

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

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Training Hyperparameters

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Summary

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Environmental Impact

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

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Technical Specifications [optional]

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