Enhance model performance with custom fine-tuning services. From data preparation to model optimization, we help you achieve superior results for your domain-specific AI applications.
Fine-tuning transforms general-purpose LLMs into specialized models tailored for your specific domain and use cases. Our process involves careful analysis of your requirements, data preparation, training dataset curation, and supervised fine-tuning to improve model performance on domain-specific tasks. This approach significantly improves accuracy, reduces hallucinations, and ensures responses align with your business context and terminology.
We employ cutting-edge fine-tuning techniques including supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), parameter-efficient methods like LoRA and QLoRA, and instruction tuning. Our approach optimizes for both performance and efficiency, reducing computational costs while achieving superior results. We also implement techniques like curriculum learning and multi-task training to enhance model capabilities across related tasks.
High-quality training data is crucial for successful fine-tuning. Our data preparation process includes data collection and curation, cleaning and preprocessing, quality assessment and filtering, format standardization, and bias detection and mitigation. We also implement data augmentation techniques and synthetic data generation to expand training datasets while maintaining quality standards and ensuring model robustness.
Comprehensive evaluation ensures your fine-tuned models meet performance requirements. We conduct rigorous testing using domain-specific benchmarks, automated evaluation metrics, human evaluation studies, A/B testing frameworks, and continuous monitoring post-deployment. Our optimization process includes hyperparameter tuning, architecture modifications, and iterative refinement to achieve optimal performance for your specific use cases.
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The amount varies by task complexity, but typically ranges from 1,000 to 100,000 high-quality examples. For specialized domains, we can achieve good results with smaller datasets using techniques like few-shot learning and data augmentation.
Fine-tuning duration depends on model size, dataset size, and computational resources. Most projects complete within 2-6 weeks, including data preparation, training, evaluation, and optimization phases.
Fine-tuning modifies the model's parameters to learn domain-specific patterns, while RAG retrieves information at inference time. Fine-tuning is better for learning specialized behavior and terminology, while RAG is ideal for incorporating frequently changing information.
We use techniques like catastrophic forgetting mitigation, continued pre-training, and mixed training datasets that include both domain-specific and general examples to maintain the model's broad capabilities.
Yes, we implement multi-task fine-tuning approaches that allow a single model to excel at multiple related tasks, sharing knowledge across tasks while maintaining task-specific performance.
Ready to create a custom LLM that understands your domain perfectly? Let our fine-tuning experts help you build models that deliver exceptional performance for your specific use cases.