How to Fine-Tune LLaMA 2 on Your Own Data for Under a Dollar

In this detailed guide, we will explore how to fine-tune LLaMA 2, a powerful language model, to act as a co-pilot for a popular fast-food chain. By leveraging PropulsionAI, we’ll demonstrate a cost-effective method to tailor LLaMA 2, ensuring it can handle specific tasks like answering frequently asked questions. This process, surprisingly affordable, will be broken down step-by-step, ensuring even beginners can follow along.

Getting Started with Data Preparation

Before diving into the fine-tuning process, it’s essential to prepare your dataset. For this tutorial, we’ve prepared a dataset that includes answers to some frequently asked questions by users of a fast-food chain. This dataset is structured to include both the questions and the appropriate responses.

If you wish to use the same dataset, you can download it from here.

Step 1: Preparing Your Dataset

1. Question and Answer Format: Ensure your dataset is in a question-and-answer format. For instance, if a user asks about gluten-free options, the dataset should include a corresponding answer detailing the available choices.

   { "prompt": "Does Domino's India offer gluten-free pizza options?", "response": "As of now, Domino's India does not provide gluten-free pizza options on their menu." }

2. Data Format: For the purposes of this tutorial, the dataset is in JSON format, making it easy to upload and process within PropulsionAI.

Fine-Tuning LLaMA 2 with PropulsionAI

The fine-tuning process is streamlined using PropulsionAI’s intuitive interface.

Here’s how you can get started:

Step 2: Creating the Dataset

1. Sign In to Propulsion AI: Log into your account and create a new project. For this example, we’ll name our project “Live Demo.”

2. Dataset Upload: Navigate to the dataset section inside your project, click on “New,” and upload your prepared dataset json. In our case, the dataset is named “Domino’s Copilot.”

3. Selecting the Data Type: Choose “Language” as the data type, given we are dealing with text data. Then, select “Conversation” to indicate the conversational nature of our dataset.

4. Mapping JSON Fields: Map your JSON fields to correspond with “Prompt” and “Response” in the Propulsion AI platform. This step is crucial for the model to understand the structure of your dataset.

Step 3: Training Your Model

1. Model Creation: Create a new model and name it appropriately. In this tutorial, we named it “Domino’s India Copilot.”

2. Version and Training: Create a new version of your model, select the LLaMA 2 model size (in this case, the 7B model), and start the training process. Remarkably, the training process is both quick and cost-effective, taking approximately 6 minutes and 44 seconds with an estimated cost of just 20 cents in this case.

Evaluating and Integrating Your Model

After the training is complete, it’s time to evaluate the performance of your fine-tuned model and integrate it into your applications.

Step 4: Model Evaluation

1. Demo Section: Use the demo section in PropulsionAI to input sample prompts and assess the responses from your fine-tuned model. This step is crucial for ensuring the model accurately answers the types of questions it will encounter.

2. Performance Assessment: Evaluate the model’s responses to ensure they meet your expectations. Adjustments can be made by further training or refining your dataset as needed.

Step 5: Integration

1. API Integration: Once satisfied with the model’s performance, head over to the API section to obtain a curl request. This request can be integrated into your applications, allowing your model to start answering user queries in real-time.

2. API Key Generation: Generate an API key from PropulsionAI to securely use your model in your applications.

Conclusion

Fine-tuning LLaMA 2 for specific tasks like answering user queries can significantly enhance your service’s responsiveness and user satisfaction. By following the steps outlined in this guide, you can customize LLaMA 2 to suit your needs affordably and efficiently. PropulsionAI not only simplifies the process but also makes it accessible for users with a limited budget, demonstrating the potential of AI in enhancing business operations.

Remember, this tutorial is just the beginning. The potential applications of fine-tuned language models are vast, from improving customer service to generating content. We encourage you to explore and innovate with LLaMA 2 and Propulsion AI.

Bonus

For those interested in taking their projects to the next level, Propulsion AI offers $25 in free credits to get started. Dive into the world of AI and discover what you can build today.