Responsible Synthetic Data Creation for Fine-Tuning with RAFT Distillation

by | Oct 12, 2024 | Technology | 0 comments

This article is contributed. See the original author and article here.

Introduction


In the age of AI and machine learning, data is the key to training and fine-tuning models. However, gathering high-quality, diverse datasets can be challenging. Synthetic data generation offers a promising solution, but how do you ensure the data you’re creating is both valid and responsible?


This blog will explore the process of crafting responsible synthetic data, evaluating it, and using it for fine-tuning models. We’ll also dive into Azure AI’s RAFT distillation recipe, a novel approach to generating synthetic datasets using Meta’s Llama 3.1 model and UC Berkeley’s Gorilla project.


 


Understanding Synthetic Data for Fine-Tuning


What is synthetic data?


Synthetic data is artificially generated rather than collected from real-world events. It is used when gathering real data is expensive, time-consuming, or raises privacy concerns. For example, synthetic images, videos, or text can be generated to mimic real-world datasets.


 


Why synthetic data matters for fine-tuning:


Fine-tuning a machine learning model with real-world data is often limited by the availability of diverse, high-quality datasets. Synthetic data fills this gap by providing additional samples, augmenting the original dataset, or generating new, unseen scenarios. For instance, in AI models like GPT or image classification systems, fine-tuning with synthetic data helps models adapt to specialized tasks or environments.


 


Common use cases:



  • Natural Language Processing (NLP): Generating new text to help models better understand uncommon language structures.

  • Computer Vision: Creating synthetic images to train models for object detection, especially in rare or sensitive cases like medical imaging.

  • Robotics: Simulating environments for AI models to interact with, reducing the need for real-world testing.


What makes data “responsible”?


Synthetic data can exacerbate existing biases or create new ethical concerns. Creating responsible data ensures that datasets are fair, and representative, and do not introduce harmful consequences when used for fine-tuning AI models.


Key principles of responsible synthetic data include:



  • Fairness: Avoiding biases in race, gender, or other sensitive attributes.

  • Privacy: Ensuring that synthetic data does not leak sensitive information from real-world datasets.

  • Transparency: Ensuring that the origins and processing of the synthetic data are documented.


Quality aspects for validation:



  • Diversity: Does the data capture the range of possible real-world cases?

  • Relevance: Does the synthetic data match the domain and task for which it will be used?

  • Performance Impact: Does the use of synthetic data improve model performance without degrading fairness or accuracy?


 Validating Synthetic Data


Validation ensures that synthetic data meets the required quality and ethical standards before being used to fine-tune a model.


Techniques for validation:



  • Ground-truth comparison: If there’s real data available, compare the synthetic data with real-world datasets to see how closely they match.

  • Model-based validation: Fine-tune a model with both synthetic and real data, then test its performance on a validation dataset. If the synthetic data significantly improves the model’s accuracy or generalization capabilities, it’s considered valid.

  • Bias and fairness evaluation: Use fairness metrics (such as demographic parity or disparate impact) to check if the synthetic data introduces unintended biases. Several tools, like Microsoft Fair Learn or IBM’s AI Fairness 360, can help identify such issues.


Tools and methods for validation:



  • Azure Machine Learning offers built-in tools for data validation, including feature importance, explainability dashboards, and fairness assessments.

  • Open-source tools such as Google’s What-If Tool or IBM AI Fairness 360, can provide detailed reports on fairness and bias in your synthetic data.


The RAFT Distillation Recipe


The RAFT distillation recipe, available on GitHub, provides a method to generate high-quality synthetic datasets using Meta Llama 3.1 and UC Berkeley’s Gorilla project.


Introduction to RAFT


RAFT (Reinforcement Active Fine-Tuning) is a technique where a pre-trained model generates synthetic data, which is then used to fine-tune the same or a similar model. The goal is to create data that is relevant, diverse, and aligned with the task for which the model is being fine-tuned.


Meta Llama 3.1:


A powerful language model deployed on Azure AI. Using RAFT, Meta Llama generates synthetic data that can be used for NLP tasks, such as question answering, summarization, or classification.


UC Berkeley’s Gorilla Project:


The Gorilla project focuses on fine-tuning models for specific tasks using minimal data. By integrating the Gorilla project’s methods into RAFT, users can create a tailored dataset quickly and efficiently.


Steps from the RAFT distillation recipe:



  • Step 1: Deploy Meta Llama 3.1 on Azure AI using the provided instructions in the GitHub repo.

  • Step 2: Use RAFT distillation to generate synthetic datasets. This involves having the model generate relevant text or data samples based on input prompts.

  • Step 3: Evaluate the generated synthetic dataset using metrics such as relevance, diversity, and performance.

  • Step 4: Fine-tune the model using the generated synthetic data to improve performance on specific tasks.


The blog can include code snippets from the repo to show users how to set up RAFT on Azure, generate synthetic datasets, and fine-tune models.


 


To create a JSONL (JSON Lines) file for training models in Azure Machine Learning, follow these step-by-step instructions:


What is a JSONL File?


A JSONL file is a format where each line is a valid JSON object. It’s commonly used for machine learning tasks like fine-tuning models because it allows you to store structured data in a readable format.


 


Step-by-Step Guide to Creating a JSONL File


Step 1: Prepare Your Data



  • Identify the data you need for fine-tuning. For instance, if you’re fine-tuning a text model, your data may consist of input and output text pairs.

  • Each line in the file should be a JSON object. A typical structure might look like this:


shardakaur_1-1727770094438.png


 


Step 2: Use a Text Editor or Python Script


You can create a JSONL file using a text editor like Notepad or VS Code, or generate it programmatically with a script (e.g., in Python).



  • Method 1: Using a Text Editor


    • Open a plain text editor (like Notepad++ or Visual Studio Code).

    • Write each line as a valid JSON object, e.g.



 


shardakaur_2-1727770166423.png


 



  •  Save the file with a .jsonl extension (e.g., training_data.jsonl).


Method 2: Using Python Script You can also use Python to generate a JSONL file, especially if you have a large dataset.


Example Python code:


shardakaur_3-1727770213960.png


 


Step 3: Validate the JSON Format


Ensure that:



  • Each line in your file is a valid JSON object.

  • There are no commas between objects (unlike a JSON array).

  • Make sure that every object is properly enclosed in {}.


Step 4: Upload to Azure ML


Once your JSON file is ready:



  1. Upload the file to your Azure Machine Learning workspace. You can do this from the Azure portal or via an SDK command.

  2. Use the file for training or evaluation in the Azure ML pipeline, depending on your task (e.g., fine-tuning).


Step 5: Test the File


To verify the file, you can use a simple Python script to load and print the contents:


 


shardakaur_0-1727792857025.png


Example: JSONL File for Fine-Tuning (with 3 lines)


 

 

shardakaur_1-1727792874445.png


 


Summary of Steps:



  1. Prepare your data in a structured JSON format.

  2. Write each line as a separate JSON object in a text editor or using a Python script.

  3. Save the file with a .jsonl extension.

  4. Validate the format to ensure each line is a valid JSON object.

  5. Upload to Azure Machine Learning for model training or fine-tuning.


By following these steps, you’ll have a valid JSONL file ready to be used in Azure Machine Learning for tasks such as model fine-tuning.


 


Resources



  1. Azure Machine Learning documentation: https://learn.microsoft.com/azure/machine-learning/?view=azureml-api-2&viewFallbackFrom=azureml-api-2%3Fstudentamb_263805

  2. Azure AI: https://azure.microsoft.com/solutions/ai/?studentamb_263805


 

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