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Fine-tuning with the kluster.ai platform＃

The kluster.ai platform provides a visual, no-code approach to fine-tuning AI models. With an intuitive interface and real-time feedback, you can train customized models without writing a single line of code.

This guide walks you through the platform's fine-tuning workflow, from uploading your training data to deploying your specialized model.

Prerequisites＃

Before getting started with fine-tuning, ensure you have the following:

  • A kluster.ai account: Sign up on the kluster.ai platform if you don't have one.
  • Prepared dataset: You need data formatted according to kluster.ai's requirements for fine-tuning (detailed below).

Supported models＃

kluster.ai currently supports fine-tuning for the following models:

Data preparation＃

High-quality, well-formatted data is crucial for successful fine-tuning:

  • Format: Data must be in JSONL format, where each line is a valid JSON object representing a training example.
  • Structure: Each JSON object should contain a messages array with system, user, and assistant messages.

  • Example format:

        {
      "messages": [
        {
          "role": "system",
          "content": "You are a JSON Generation Specialist. Convert user requests into properly formatted JSON."
        },
        {
          "role": "user",
          "content": "Create a configuration for a web application with name 'TaskMaster', version 1.2.0, and environment set to development."
        },
        {
          "role": "assistant",
          "content": "{\n  \"application\": {\n    \"name\": \"TaskMaster\",\n    \"version\": \"1.2.0\",\n    \"environment\": \"development\"\n  }\n}"
        }
      ]
    }

  • Quantity: The minimum requirement is 10 examples, but more diverse and high-quality examples yield better results.

  • Quality: Ensure your data accurately represents the task you want the model to perform.

Data preparation

For a detailed walkthrough of data preparation, see the Fine-tuning Sentiment Analysis Tutorial.

Find Llama datasets on Hugging Face

There is a wide range of datasets suitable for Llama model fine-tuning on Hugging Face Datasets. Browse trending and community-curated datasets to accelerate your data preparation.

Fine-tuning workflow＃

The process of fine-tuning a model using the kluster.ai platform interface involves several key steps which will be described in more detail in the following sections.

Navigate to the fine-tuning page＃

To begin, visit the kluster.ai fine-tuning page and click the Create new job button.

Create new fine-tuning job

Choose model and upload data＃

  1. Select a base model: Choose one of the available foundation models from the dropdown menu.
  2. Upload your file: Upload your prepared JSONL training file by dragging and dropping the file or using the file selection dialog.

Select base model

Validation data

You can optionally upload a validation dataset in the same format as your training data. This helps evaluate the performance of your model during training.

Configure hyperparameters＃

Customize your fine-tuning job by configuring these settings:

  1. Nickname: Add an optional custom suffix that will be appended to your fine-tuned model name.
  2. Batch size: Control how many examples are processed in each training step.
  3. Learning rate multiplier: Adjust how quickly the model adapts to your training data.
  4. Number of epochs: Define how many times the model will cycle through your entire dataset.
  5. Create: Click the Create button to start the fine-tuning process.

Configure hyperparameters

Monitor job progress＃

After submitting your fine-tuning job, you can monitor the status and progress of your job on the fine-tuning page.

Each job displays information including:

  • Job ID
  • Base model
  • Training method
  • Creation date
  • Current status
  • Training metrics (when complete)

Job Progress

Status Update

The job status updates will first display "queued," then "running," and "succeeded" when complete.

Access your fine-tuned model＃

Once fine-tuning is complete, your custom model will be listed on the fine-tuning page with its unique identifier and available in the model selection dropdown in the playground.

Playground Chat

Benefits of fine-tuning＃

Fine-tuning offers several advantages over using general-purpose models:

  • Improved performance: Fine-tuned models often outperform base models on specific tasks.
  • Cost efficiency: Smaller fine-tuned models can outperform larger models at a lower cost.
  • Reduced latency: Fine-tuned models can deliver faster responses for your applications.
  • Consistency: More reliable outputs tailored to your specific task or domain.

Next steps＃