Slim Sql 1b V0

🚀 Model Card for Model ID

slim-sql-1b-v0 is the first model in the SLIM (Specialized Language Instruct Model) series, which is designed to generate accurate SQL queries for data retrieval on simple table structures given a natural language prompt.

🚀 Quick Start

The fastest way to get started with slim is through direct import in transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM  
tokenizer = AutoTokenizer.from_pretrained("slim-sql-1b-v0")  
model = AutoModelForCausalLM.from_pretrained("slim-sql-1b-v0")

Please refer to the generation_test.py files in the Files repository, which includes 100 samples and script to test the model.

The sql-slim model was fine-tuned with a simple "<human> and <bot> wrapper", so to get the best results, wrap inference entries as:

full_prompt = "<human>: " + my_prompt + "\n" + "<bot>:"

The prompt consists of two sub-parts:

1. Table creation prompt providing table name, variables, and variable type.
2. Specific question or instruction based on the text passage

Test sample example: {"context": "CREATE TABLE table_name_34 (season VARCHAR, lost VARCHAR, points VARCHAR)", "question": "Which season did the Minnesota Kicks lose 13 games and score 156 points?", "answer": "SELECT COUNT(season) FROM table_name_34 WHERE lost = 13 AND points = 156"} A subset of test samples are provided in this repo (sql_test_100_simple_s).

For use in training, the "<human>" tag would be associated with "context" and "question" statements, while the "<bot>" tag will be associated with the model's output.

If you are using a HuggingFace generation script:

# prepare prompt packaging used in fine-tuning process
new_prompt = "<human>: " + entries["context"] + "\n" + entries["query"] + "\n" + "<bot>:"

inputs = tokenizer(new_prompt, return_tensors="pt")  
start_of_output = len(inputs.input_ids[0])

#   temperature: set at 0.3 for consistency of output
#   max_new_tokens:  set at 100 - may prematurely stop a few of the summaries

outputs = model.generate(
        inputs.input_ids.to(device),
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        temperature=0.3,
        max_new_tokens=100,
        )

output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True)

✨ Features

Benchmark Tests

Evaluated against 100 test SQL queries with under 100 characters. 1 point given for exact string match, 0 given for incorrect answer.

• Accuracy Score: 86 correct out of 100
• 8 incorrect answers attributed to query structure ordering or naming convention differences
• 6 incorrect answers attributed to incorrect variable selection or aggregate function use

Model Description

• Developed by: llmware
• Model type: TinyLlama
• Language(s) (NLP): English
• License: apache-2.0
• Finetuned from model: TinyLlama-1.1b - 2.5T checkpoint

Direct Use

slim-sql-1b-v0 is designed to generate accurate SQL queries for data retrieval on simple table structures given a natural language prompt. For best results, prompts should be structured as a question to retrieve information and perform aggregate functions on one or several variables.

📚 Documentation

Bias, Risks, and Limitations

Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms.

📄 License

This model is licensed under the apache-2.0 license.

Model Card Contact

Dylan Oberst & llmware team