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license: apache-2.0 |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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slim-sql-1b-v0 is the first model in the SLIM (Specialized Language Instruct Model) series. |
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### Benchmark Tests |
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Evaluated against 100 test SQL queries with under 100 characters. 1 point given for exact string match, 0 given for incorrect answer. |
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--**Accuracy Score**: **86** correct out of 100 |
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- 8 incorrect answers attributed to query structure ordering or naming convention differences |
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- 6 incorrect answers attributed to incorrect variable selection or aggregate function use |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** llmware |
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- **Model type:** TinyLlama |
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- **Language(s) (NLP):** English |
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- **License:** apache-2.0 |
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- **Finetuned from model:** [TinyLlama-1.1b - 2.5T checkpoint](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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slim-sql-1b-v0 is designed to generate accurate SQL queries for data retrieval on simple table structures given a natural language prompt. |
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For best results, prompts should be structured as a question to retrieve information and perform aggregate functions on one or several variables. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms. |
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## How to Get Started with the Model |
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The fastest way to get started with slim is through direct import in transformers: |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("slim-sql-1b-v0") |
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model = AutoModelForCausalLM.from_pretrained("slim-sql-1b-v0") |
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Please refer to the generation_test.py files in the Files repository, which includes 100 samples and script to test the model. |
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The sql-slim model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as: |
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full_prompt = "<human>: " + my_prompt + "\n" + "<bot>:" |
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The prompt consists of two sub-parts: |
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1. Table creation prompt providing table name, variables, and variable type. |
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2. Specific question or instruction based on the text passage |
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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"} |
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A subset of test samples are provided in this repo ("sql_test_100_simple_s"). |
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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. |
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If you are using a HuggingFace generation script: |
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# prepare prompt packaging used in fine-tuning process |
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new_prompt = "<human>: " + entries["context"] + "\n" + entries["query"] + "\n" + "<bot>:" |
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inputs = tokenizer(new_prompt, return_tensors="pt") |
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start_of_output = len(inputs.input_ids[0]) |
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# temperature: set at 0.3 for consistency of output |
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# max_new_tokens: set at 100 - may prematurely stop a few of the summaries |
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outputs = model.generate( |
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inputs.input_ids.to(device), |
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eos_token_id=tokenizer.eos_token_id, |
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pad_token_id=tokenizer.eos_token_id, |
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do_sample=True, |
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temperature=0.3, |
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max_new_tokens=100, |
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) |
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output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True) |
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## Model Card Contact |
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Dylan Oberst & llmware team |
