Fine Tuning
gllm-training | Fine tuning Guidelines
What is a Fine Tuning ?
Fine tuning is a process that adapts a pre-trained model to perform better on specific tasks or domains by training it on a smaller, specialized dataset.This ensures that the model’s responses are more accurate, relevant, and tailored to particular use cases or requirements. The fine-tuning techniques used in our SDK include:
Supervised Fine Tuning - training models using labeled input-output pairs to achieve task-specific performance improvements.
Group Relative Policy Optimization (GRPO) - A reinforcement learning-based method that trains models to maximize reward functions across groups of candidate responses. This enables models to learn preference-aligned behaviors directly from reward signals instead of relying on explicit input-output pairs.
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