Unlock your AI models' full potential through precision fine-tuning. Optimize your models for results that truly matter to your specific needs. Remember, it's not about more data β it's about the right data.
Start by creating a new tuning model. You can choose from GPT-4o or GPT-4o-mini as the base model.
Use our intuitive tuning editor to upload tuning data for the model. Track the progress and review the results all within the dashboard.
Once your fine-tuned model is ready, you can seamlessly integrate it into your prompts, workflows and RAG chat agents with one click.
Our custom tools help you grab info from websites, handle big datasets, and build smart AI workflows. Save time and create better content by tapping into your data.
Fine-tuning is the process of adapting a pre-trained AI model to perform specific tasks by training it on a focused dataset. Think of it as teaching an already smart AI to become an expert in your particular domain or use case. With Fetch Hive's fine-tuning tools, you can customize leading models like GPT-4o to better understand your company's terminology, style, and knowledge areas, resulting in more accurate, relevant, and consistent outputs for your specific needs.
While both prompts and fine-tuning help customize AI outputs, they work differently:
Fine-tuning is ideal when you need consistent, specialized behavior that would require extremely long or complex prompts to achieve. It's particularly valuable for maintaining brand voice, following specific formats, or handling specialized knowledge that general models struggle with.
Effective fine-tuning requires quality training data that represents the tasks you want your model to perform. Your training data should include:
Fetch Hive simplifies this process with tools to help you prepare, format, and validate your training data before launching the fine-tuning process. Remember, the quality of your training data directly impacts the quality of your fine-tuned model.
Fetch Hive currently supports fine-tuning for OpenAI's cutting-edge models:
We regularly expand our fine-tuning options as new models become available. Our platform handles all the technical complexity of the fine-tuning process, allowing you to focus on your data and use cases rather than infrastructure management.
Fetch Hive provides several ways to evaluate and improve your fine-tuned models:
We recommend setting aside a portion of your examples as a test set (not used in training) to properly evaluate how well your model generalizes to new inputs. Our platform makes it easy to track improvements across different versions of your fine-tuned models.