How to Prepare a Dataset for Fine-tuning on Hyperstack AI Studio

Hyperstack AI Studio is a powerful end-to-end Gen AI platform that simplifies every step of the AI workflow, from fine-tuning and performance evaluation to deployment. 

But before you begin, it’s important to prepare your dataset correctly. Even the most advanced AI models are only as good as the data they are trained on. And poorly formatted data can cause errors, slow training and reduce model performance.

In this blog, we’ll take you step by step through preparing your dataset for fine-tuning on Hyperstack AI Studio for a smooth and successful workflow.

Why Dataset Preparation Matters

For fine-tuning LLMs, the format and quality of your dataset can make or break your outcome. A well-prepared dataset ensures:

• Smooth ingestion: The Hyperstack AI Studio platform automatically validates your JSONL files, preventing errors during upload.
• Better model understanding: Structured data helps your model learn meaningful patterns.
• Faster fine-tuning: Clean datasets reduce preprocessing overhead and improve training speed.
• Higher accuracy: Well-structured datasets can help generate more relevant and context-aware responses.

Choosing the Right File Format: JSONL

Hyperstack AI Studio requires datasets for fine-tuning to be in JSONL (JSON Lines) format. But what does this mean? A standard JSON files wrap all objects in an array, while a JSONL allows each JSON object to exist independently on a single line. This makes it much easier to process large datasets efficiently.

Key Components of JSONL for Fine-tuning

When preparing your dataset for fine-tuning, understanding the structure of a JSONL file is important. Each line in your .jsonl file represents a single conversation and the way it is structured directly impacts how your model learns. 

The core structure revolves around the messages array, which contains all the turns in a conversation. Each message within the array has two essential elements: role and content. The role specifies who is “speaking” in that part of the conversation. 

Elements of a JSONL File

Every line in your JSONL file must be a valid JSON object containing:

• “messages”: An array of message objects.

And each message object must have the following:

1. "system": You can use either "system", "user" or "assistant".
2. "content": The actual text of the message, such as instructions, questions or responses.

Here’s an example of a simple and well-structured conversation:

{"messages": [{"role": "user", "content": "Which GPU is best for AI training in 2025?"}, {"role": "assistant", "content": "The NVIDIA H100 and A100 GPUs are top choices for AI training due to their high memory bandwidth, tensor cores, and scalability for large LLMs."}]}

{"messages": [{"role": "system", "content": "You are an AI hardware expert."}, {"role": "user", "content": "Which GPU is best for AI training in 2025?"}, {"role": "assistant", "content": "The NVIDIA H100 and A100 GPUs are top choices for AI training due to their high memory bandwidth, tensor cores, and scalability for large LLMs."}]}

In this example:

• The system role sets the stage for the assistant to act as an AI hardware expert.
• The user role provides the query: “Which GPU is best for AI training in 2025?”
• The assistant role shows the expected response: “The NVIDIA H100 and A100 GPUs are top choices for AI training due to their high memory bandwidth, tensor cores, and scalability for large LLMs.”

JSONL File Formatting Checklist

Here is a quick checklist to ensure your JSONL file is valid and ready to use:

1. One JSON object per line: Each line must represent a complete JSON object, no splitting across multiple lines.
2. Proper JSON formatting: You must follow the right JSON formatting for your dataset mentioned in this guide.
3. Required fields: Each message must include the required fields, including messages, system and content.
   No trailing commas: Don’t leave extra commas at the end of objects or arrays; this will break parsing.
4. UTF-8 encoding: Save your JSONL file in UTF-8 format to avoid hidden character issues.

*AI Studio currently supports a maximum context length of 8,192 tokens (including system prompt, prior messages, input and output).

Uploading Your Dataset to AI Studio

Once your dataset is ready in the right format, follow these steps to upload it to the Hyperstack AI Studio:

Step 1: Open the Logs Page

Visit the Logs & Datasets page in AI Studio, where you can manage and view all your uploaded data and logs.

Step 2: Upload Your .jsonl File

1. Ensure your file meets JSONL format guidelines.
2. Click Upload Logs in the top-right corner.
3. Either select your .jsonl file or drag-and-drop it.

Don’t have your own dataset yet? Use our sample data here to get started. 

Step 3: Add Tags

1. Enter at least one tag to categorise your logs (e.g., support, finance, testing).
2. Tags help with searching, filtering and organising your datasets later.

Step 4: Validate and Upload

Click on Validate and Upload. The system will check your file format and structure, then upload your data if validation succeeds.

Try Hyperstack AI Studio Today

Ready to build with Gen AI? Sign in to Hyperstack, upload your dataset and start fine-tuning popular open-source LLMs like Llama and Mistral. Experience the power of streamlined AI development, easy dataset management and fast model deployment, all in one platform.

FAQs

What is JSONL format?

JSONL (JSON Lines) is a format where each line is a separate JSON object, making datasets efficient and easy to process.

How do I access AI Studio on Hyperstack?

Sign in using your credentials or SSO, add billing credit and navigate to AI Studio in your Hyperstack account.

What is the required dataset format for fine-tuning in Hyperstack AI Studio?

Hyperstack AI Studio requires datasets in JSONL format, where each line is a valid JSON object containing role and content fields.

How do I upload a dataset to Hyperstack AI Studio?

Go to Logs & Datasets, click Upload Logs, add tags, validate and then upload your JSONL dataset file.

Can I use sample data if I don’t have my own dataset yet?

Yes, Hyperstack AI Studio provides a sample dataset you can use to start experimenting with fine-tuning immediately.

Why should I validate my JSONL file before uploading?

Validation ensures proper formatting, prevents errors during ingestion and improves the chances of successful fine-tuning runs.