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license: apache-2.0
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# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

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

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** llmware
- **Model type:** TinyLlama
- **Language(s) (NLP):** English
- **License:** apache-2.0
- **Finetuned from model:** [TinyLlama-1.1b - 2.5T checkpoint](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T)


### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

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

<!-- This section is meant to convey both technical and sociotechnical 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