MLX Fine-Tuning: Transforming Questions into SQL Queries

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Introduction to MLX Fine-Tuning

In the rapidly evolving field of natural language processing, the ability to convert human language into structured queries is a game-changing capability. This article delves into the process of fine-tuning a large language model (LLM) using MLX, a powerful tool designed for running and fine-tuning LLMs on Apple Mac computers. We'll focus on adapting the Mistral 7B parameter model to transform natural language questions into SQL queries, a task with wide-ranging applications in database management and information retrieval.

Understanding MLX and Its Capabilities

MLX is a package that enables users to run and fine-tune large language models on Apple Silicon Macs. This tool opens up new possibilities for developers and researchers who want to leverage the power of LLMs without the need for extensive cloud computing resources.

Key Features of MLX:

Local Processing: Runs entirely on Mac computers, utilizing Apple's hardware efficiently.
Fine-Tuning Capabilities: Allows customization of pre-trained models for specific tasks.
Optimized Performance: Designed to make the most of Apple's M-series chips.

The Goal: From Natural Language to SQL

Our objective is to fine-tune the Mistral 7B model to perform a specific task: converting natural language questions about data into SQL queries. This capability is invaluable for:

Database administrators who need to quickly generate queries from user requests
Data analysts who want to streamline their workflow
Non-technical users who need to interact with databases without knowing SQL

For example, we want the model to transform a question like "What are the notes for South Australia?" into the corresponding SQL query:

SELECT notes FROM table WHERE current_slogan = 'South Australia'

Step-by-Step Guide to MLX Fine-Tuning

1. Setting Up the Environment

Before we begin the fine-tuning process, we need to set up our development environment. Follow these steps:

Clone the MLX examples repository:

git clone https://github.com/ml-explore/mlx-examples.git

Navigate to the correct directory:
```
cd mlx-examples/llms/
```

Create and activate a Conda environment:

conda create -n mlx python=3.11
conda activate mlx

Install the required packages:

pip install -r requirements.txt
pip install mlx-lm

If prompted, agree to the Xcode build license:
```
sudo xcodebuild -license
```

2. Preparing the Data

Data preparation is a crucial step in the fine-tuning process. We'll be using the WikiSQL dataset, which contains pairs of natural language questions and their corresponding SQL queries.

Data Format

The original data is in CSV format with columns for questions, tables, and SQL queries. We need to transform this into a format suitable for training our model. Each entry in our training data will be a JSON object containing:

The question
The table structure
The corresponding SQL query

Data Transformation

We'll merge the table information, columns, question, and answer into a single line for each entry. This process converts the CSV file into a JSONL (JSON Lines) format, where each line is a valid JSON object.

Example of a transformed data entry:

{"table": "table_name", "columns": ["col1", "col2", "col3"], "question": "What is the value of col1?", "answer": "SELECT col1 FROM table_name"}

Data Files

After transformation, we'll have three main data files:

train.jsonl: Contains 80,000 training examples
valid.jsonl: Contains 100 validation examples
test.jsonl: Contains 100 test examples

These files are already prepared and available in the data folder of the MLX examples repository.

3. Downloading the Pre-trained Model

Before fine-tuning, we need to download a quantized version of the Mistral 7B model. Use the following command:

python convert.py --hf-path mistralai/Mistral-7B-v0.1 -q

This command downloads and quantizes the model, preparing it for fine-tuning on your Mac.

4. Training the Model

Now that we have our data prepared and the pre-trained model downloaded, we can start the fine-tuning process.

Training Command

Use the following command to start training:

python lora.py --model mistralai/Mistral-7B-v0.1 --train --batch-size 1 --lora-layers 4

Training Parameters Explained

--model: Specifies the pre-trained model we're using (Mistral 7B)
--train: Indicates that we're in training mode
--batch-size 1: Sets the batch size to 1 (adjust based on your RAM)
--lora-layers 4: Specifies the number of LoRA layers to use

Training Process

During training, you'll see output indicating the progress:

The model will save adapters every 200 steps
You'll observe GPU and CPU usage statistics
The process may take several minutes to complete, depending on your hardware

5. Evaluating the Model

After training, it's essential to evaluate the model's performance on the test set.

Evaluation Command

Use this command to evaluate the fine-tuned model:

python lora.py --model mistralai/Mistral-7B-v0.1 --adapter-file adapters.npz --test

Interpreting the Results

The evaluation will provide a test loss value. A lower loss indicates better performance. For example, a test loss of 1.5 suggests that the model has learned to generate SQL queries with reasonable accuracy.

6. Testing the Fine-tuned Model

To see how well the model performs after fine-tuning, we can test it with a sample question.

Test Command

python lora.py --model mistralai/Mistral-7B-v0.1 --adapter-file adapters.npz --max-tokens 100 --prompt "What is Terren Rose's nationality?"

Interpreting the Output

The model should now generate an SQL query in response to the natural language question. For example:

SELECT nationality FROM table WHERE name = 'Terren Rose'

This output demonstrates that the model has learned to convert questions into SQL queries, although it may still require some refinement for perfect accuracy.

7. Uploading the Model to Hugging Face

Sharing your fine-tuned model with the community is a great way to contribute to the field of NLP.

Preparation

Prepare the upload command:

python -m mlx_lm.convert --hf-path mistralai/Mistral-7B-v0.1 -q --upload-repo your-username/your-repo-name

Replace your-username/your-repo-name with your desired repository name.

Uploading Process

Execute the command, and the model will be uploaded to Hugging Face, making it accessible to the community.

Advanced Techniques and Considerations

Optimizing for Different Hardware Configurations

The fine-tuning process can be optimized based on your specific hardware:

For Macs with more RAM, increase the batch-size and lora-layers values
Experiment with different quantization levels to balance performance and accuracy

Handling Different Types of Questions

To improve the model's versatility, consider fine-tuning on a diverse set of questions:

Simple selection queries
Queries with multiple conditions
Aggregation queries (COUNT, SUM, AVG)
Queries involving joins between tables

Improving SQL Generation Accuracy

To enhance the accuracy of SQL generation:

Increase the size of the training dataset
Implement a post-processing step to validate and correct SQL syntax
Fine-tune on domain-specific data if targeting a particular industry or application

Integrating with Database Systems

For practical applications, consider:

Developing an interface that connects the model output to a real database
Implementing safety checks to prevent harmful queries from being executed
Creating a feedback loop where query results are used to improve the model

Potential Applications

1. Database Management Systems

Integrate the fine-tuned model into database management tools to allow users to query databases using natural language. This can significantly lower the barrier to entry for data analysis tasks.

2. Business Intelligence Tools

Enhance BI platforms with natural language interfaces, enabling non-technical users to generate reports and visualizations without writing SQL.

3. Customer Support Systems

Implement the model in customer support chatbots to quickly retrieve information from databases based on customer queries.

4. Educational Tools

Create interactive learning platforms that help students understand the relationship between natural language and SQL queries.

5. Data Exploration Tools

Develop tools that allow data scientists and analysts to quickly explore large datasets using natural language commands.

Challenges and Limitations

1. Complex Query Generation

While the model can handle simple queries well, generating complex SQL involving multiple joins, subqueries, or advanced functions may still be challenging.

2. Context Understanding

The model may struggle with questions that require understanding of broader context or domain-specific knowledge not present in the training data.

3. Ambiguity in Natural Language

Natural language can be ambiguous, and the model may sometimes misinterpret the user's intent, leading to incorrect SQL queries.

4. Data Privacy Concerns

When fine-tuning models on sensitive data, care must be taken to ensure that private information is not leaked through the model's outputs.

5. Resource Intensiveness

Fine-tuning and running large language models can be resource-intensive, which may limit deployment options, especially for smaller organizations.

Future Directions

1. Multi-modal Models

Explore the integration of text and visual inputs to generate SQL queries based on both natural language and database schema diagrams.

2. Continuous Learning

Implement systems that allow the model to learn from user feedback and corrections, continuously improving its performance over time.

3. Cross-database Compatibility

Extend the model's capabilities to generate SQL for different database systems, accounting for syntax variations.

4. Natural Language Generation of Query Results

Develop models that not only generate SQL from natural language but also translate query results back into easily understandable natural language summaries.

5. Integration with Code Generation

Expand the model's capabilities to generate not just SQL queries but also accompanying code in various programming languages for data processing and analysis.

Conclusion

Fine-tuning the Mistral 7B model using MLX to convert natural language questions into SQL queries represents a significant step forward in making databases more accessible to a wider audience. This process, which involves careful data preparation, model training, and evaluation, opens up new possibilities for interacting with data.

By following the steps outlined in this guide, developers and researchers can create powerful tools that bridge the gap between human language and database queries. The potential applications span various industries, from business intelligence to education and customer support.

However, it's important to recognize the current limitations and challenges, such as handling complex queries and ensuring data privacy. As the field progresses, we can expect to see more advanced models that address these issues and push the boundaries of what's possible in natural language database interactions.

The journey of fine-tuning models like Mistral 7B is just beginning, and the future holds exciting possibilities for even more sophisticated and user-friendly data interaction tools. As we continue to refine these models and develop new techniques, the dream of truly conversational data analysis comes ever closer to reality.

Article created from: https://youtu.be/sI1uKhagm7c?si=AzDHu9_KXljh9JQ_