llmware/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.

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.

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.

How to Get Started with the Model

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)  

Model Card Contact

Dylan Oberst & llmware team