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:
- Prepare your dataset: Collect representative examples for the task and save them as a JSON Lines (
.jsonl
) file. - Upload the dataset: Use the Platform “Upload” dialog or call
files.upload
via the API to receive afile_id
. - 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.
- Monitor training: Track status and metrics in the dashboard or poll
fine_tuning.jobs.retrieve
until the job reachessucceeded
orfailed
. - Retrieve the fine-tuned model: When the job finishes, grab the returned
fine_tuned_model
ID and treat it like any other model. - 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.
- 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.
-
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.
Additional resources#
- Step-by-step tutorial: Learn the fundamentals with our Fine-tuning sentiment analysis tutorial.
- Available models: Explore our Models page to see all foundation models that support fine-tuning.
- API reference: Review the complete API documentation for all fine-tuning related endpoints.