Preparing Fine-Tuning Datasets with Open Source Models: A Step-by-Step Guide

Introduction to Fine-Tuning Dataset Preparation

Preparing high-quality datasets for fine-tuning language models is a critical step in developing custom AI applications. While previous methods often relied on proprietary services like OpenAI, this guide focuses on using open-source models, particularly Llama 2, to clean and prepare datasets for fine-tuning chat models.

This approach is especially valuable for those developing commercial models, as it avoids the licensing restrictions associated with using proprietary services for dataset preparation. We'll walk through the entire process, from prompt preparation to setting up a Llama 2 server and running automated scripts to convert raw data into question-answer formats suitable for fine-tuning.

Why Use Open-Source Models for Dataset Preparation?

Using open-source models like Llama 2 for dataset preparation offers several advantages:

1. Licensing flexibility: Avoid restrictions on commercial use that come with proprietary services.
2. Cost-effective: Reduce expenses associated with API calls to paid services.
3. Customization: Greater control over the model and process, allowing for tailored solutions.
4. Learning opportunity: Gain insights into the inner workings of language models and fine-tuning processes.

Setting Up the Environment

Before diving into the dataset preparation process, it's essential to set up the necessary environment. This includes:

1. Access to a Llama 2 70B model
2. A server capable of running the model (e.g., RunPod)
3. Python scripts for data processing
4. Raw dataset for fine-tuning

For this guide, we'll be using a dataset of touch rugby rules as our example. This sport is relatively obscure, making it an excellent test case for fine-tuning a language model on specialized knowledge.

Prompt Preparation for Question-Answer Generation

The first step in our process is to prepare a prompt that will instruct the language model to generate question-answer pairs from raw text. This prompt consists of several key components:

1. Input text: A chunk of raw data from our dataset
2. Context: Information about the subject matter to frame the questions correctly
3. Request: Specific instructions for generating question-answer pairs
4. Example: A sample of the desired output format

Here's a breakdown of each component:

Input Text

This is a raw chunk of text from our dataset. For example:

1. DEFINITIONS AND TERMINOLOGY
1.1 ATTACKING SCORING LINE
The Attacking Scoring Line is the line on or over which a team has to place the ball to score a Touchdown.
1.2 ATTACKING TEAM
The Attacking Team is the team which has possession or is gaining possession of the ball.

Context

Provide context to ensure the model understands the subject matter:

This text is from the official rules of touch rugby, a limited-contact version of rugby. The rules cover various aspects of the game, including field dimensions, scoring, player roles, and general gameplay.

Request

Clear instructions for the model on how to generate question-answer pairs:

Provide five question and answer pairs based on the text above. The questions must begin with "In the context of touch rugby". The answers should borrow verbatim from the text above. In providing each question, consider that the reader does not see or have access to any of the other questions for context. Vary the style and format of questions. Respond in plain text, with a new line for each question and answer. Do not include question numbers.

Example

A sample of the desired output format:

In the context of touch rugby, what does the "half" refer to?
The half refers to the player who takes possession following a rollball.

In the context of touch rugby, what is the purpose of the playing rules?
The purpose is to provide a standardized set of rules for the sport of touch football.

Setting Up a Llama 70B Server on RunPod

To run our data preparation scripts, we need a server capable of hosting the Llama 2 70B model. RunPod offers a convenient solution for this. Here's how to set it up:

1. Create an account or log in at RunPod.io
2. Navigate to the "Secure Cloud" section
3. Select two RTX A6000 GPUs for a balance of speed and cost
4. Search for and select the "Trellis Research" Llama 70B option
5. Deploy the server

Once your server is running, note the RunPod ID. You'll need this for your scripts.

Automated Script for Dataset Conversion

With our server set up, we can now use automated scripts to convert our raw dataset into a question-answer format. The main script, create_qa.py, handles this process. Here's an overview of how it works:

1. The script takes chunks of raw text from the input dataset
2. It sends these chunks to the Llama 2 server using the prepared prompt
3. The server generates question-answer pairs based on the input
4. The script collects and formats the responses

To run the script:

1. Ensure your .env file contains the correct RunPod ID
2. Run the command: python create_qa.py

The script allows you to process either a single chunk (for testing) or the entire dataset. When processing the full dataset, it uses parallel requests to improve efficiency.

Converting Question-Answer Pairs to CSV Format

After generating the question-answer pairs, we need to convert them into a format suitable for fine-tuning. The qa_to_csv.py script handles this conversion:

1. It takes the generated question-answer pairs
2. Formats them into a CSV file with "prompt" and "completion" columns
3. Saves the result as a training dataset

To run this script:

python qa_to_csv.py

The resulting CSV file will contain rows like this:

prompt,completion
"In the context of touch rugby, what's the dead ball line?","The dead ball line is the line that marks the end of the field of play."

This format is ideal for fine-tuning chat models, as it clearly separates the input (prompt) from the expected output (completion).

Optimizing the Process

To make the most of this data preparation method, consider the following optimization techniques:

Parallelization of Requests

Instead of processing one chunk at a time, send multiple parallel requests to your Llama 2 server. This significantly increases throughput, potentially matching the speed of proprietary services like OpenAI (within rate limits).

Balancing Chunk Size and VRAM Usage

There's a trade-off between the size of text chunks you process and the VRAM usage on your GPU server:

• Larger chunks or batch sizes provide more context but consume more VRAM
• Smaller chunks or batches use less VRAM but may lack context for generating optimal questions

Monitor your GPU memory usage and adjust accordingly. A batch size of 8 and a context length of 500 tokens often provide a good balance.

Licensing Considerations

When using open-source models for dataset preparation, it's crucial to be aware of licensing implications:

• Llama 2 allows you to use it for preparing datasets, but only for training other Llama models
• For more flexibility, consider using MPT models
• Be cautious with models like Falcon, as some of their training datasets have commercial use limitations

If you're specifically training Llama models, using Llama 2 for dataset preparation is a straightforward choice. For maximum flexibility, especially for commercial applications, MPT models may be the safest option.

Conclusion

Preparing fine-tuning datasets using open-source models like Llama 2 offers a powerful and flexible alternative to proprietary services. By following the steps outlined in this guide, you can create high-quality question-answer datasets suitable for fine-tuning chat models, all while maintaining control over your data and avoiding licensing restrictions.

Key takeaways:

1. Use open-source models to clean and prepare datasets for fine-tuning
2. Set up a dedicated server (e.g., on RunPod) to run large language models
3. Develop clear and structured prompts for generating question-answer pairs
4. Utilize automated scripts to process large datasets efficiently
5. Optimize your process through parallelization and careful resource management
6. Be mindful of licensing implications when choosing models for dataset preparation

By mastering these techniques, you'll be well-equipped to create custom, high-quality datasets for fine-tuning language models, opening up new possibilities for AI application development across various domains.

Further Resources

To deepen your understanding of fine-tuning and dataset preparation, consider exploring:

• Documentation for Llama 2 and other open-source language models
• Research papers on efficient fine-tuning techniques
• Community forums and discussions on AI model development
• Tutorials on advanced prompt engineering for specialized tasks

Remember, the field of AI and language models is rapidly evolving. Stay curious, experiment with different approaches, and always keep an eye on the latest developments in the field. With these tools and techniques at your disposal, you're well-positioned to create powerful, customized AI models tailored to your specific needs.

Article created from: https://www.youtube.com/watch?v=JJ5mcdEIbj8