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Overview of fine-tuning models＃

Fine-tuning lets you transform general-purpose models into specialized AI assistants that excel at your unique tasks. With kluster.ai, you can fine-tune models using either the platform or the API—choose the path that fits your workflow and technical needs.

Fine-tuning flow＃

Fine-tuning on kluster.ai follows a simple, seven-step loop:

  1. Prepare your dataset: Collect representative examples for the task and save them as a JSON Lines (.jsonl) file.
  2. Upload the dataset: Use the Platform “Upload” dialog or call files.upload via the API to receive a file_id.
  3. Configure & launch a job: Choose a base model, set LoRA-specific (Low-Rank Adaptation) hyper-parameters (epochs, learning rate, adapter rank, etc.), and start the job.
  4. Monitor training: Track status and metrics in the dashboard or poll fine_tuning.jobs.retrieve until the job reaches succeeded or failed.
  5. Retrieve the fine-tuned model: When the job finishes, grab the returned fine_tuned_model ID and treat it like any other model.
  6. Evaluate & iterate: Test the model on unseen prompts, compare against the base model, and re-run fine-tuning with refined data or parameters if needed.
  7. Deploy & integrate: Call the model in production, export its LoRA adapter, or share it with teammates through kluster.ai’s model registry.

Fine-tuning is your go-to when you need reliable, domain-specific outputs (e.g., JSON-formatted responses, brand-aligned tone) that prompt engineering alone can’t guarantee.

When to fine-tune your model＃

Fine-tuning is ideal for scenarios where you need:

  • Domain specialization: Create models that excel in specific fields like medicine, law, finance, or technical documentation.
  • Brand-aligned responses: Train models to match your company's voice, style, and communication guidelines.
  • Format consistency: Ensure reliable output in specific formats like JSON, XML, or Markdown.
  • Enhanced reasoning: Improve analytical capabilities for specific types of problems.
  • Custom behavior: Develop assistants that follow your unique processes and workflows.

Benefits of fine-tuning＃

Fine-tuning delivers several key advantages over using general-purpose models:

  • Improved performance: Fine-tuned models consistently outperform base models on specific tasks.
  • Cost efficiency: Smaller fine-tuned models can match or exceed the performance of larger models at a lower cost.
  • Reduced latency: Fine-tuned models provide faster responses, enhancing the user experience.
  • Consistency: Achieve more reliable outputs tailored to your specific requirements.
  • Data privacy: Train models on your data without exposing sensitive information in prompts.

Supported models＃

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

Choose your fine-tuning approach＃

kluster.ai offers two ways to fine-tune models, each designed for different user preferences and requirements:

  • Guide Platform

    Use the platform to fine-tune without writing code. The platform is ideal for users who want a guided, interactive experience and real-time feedback on training progress.

    Visit the guide

  • Guide API

    Fine-tune models with code for maximum flexibility and automation. The API is best for developers who need advanced customization, integration, or workflow automation.

    Visit the guide

Additional resources＃