Can You Fine-Tune All Models in Bedrock, or Only Certain Ones? No, you cannot fine-tune all models available in AWS Bedrock. Fine-tuning is only supported for specific models, depending on the model provider and their technical constraints. For example, Amazon Titan Text and Cohere Command are examples of models that currently support fine-tuning in Bedrock, while others (like Anthropic’s Claude or Stability AI’s image models) do not. This limitation exists because fine-tuning requires the model architecture to support parameter updates, and providers must expose APIs or workflows to enable it. Always check AWS’s official documentation for the latest list of fine-tunable models, as capabilities evolve over time.
How to Select a Model for Fine-Tuning Choosing the right model depends on your use case, data, and performance requirements. Start by identifying the task: for text generation (e.g., chatbots), Titan Text or Cohere Command might be suitable. For classification or summarization, evaluate models based on their baseline performance in those areas. Next, consider dataset compatibility: if your data is domain-specific (e.g., medical text), pick a model that aligns with that domain or has shown adaptability via fine-tuning. Smaller models (like Titan Text Lite) may suffice for simpler tasks, while larger ones (Titan Text Express) are better for complex reasoning but cost more to train and deploy.
Practical Considerations Cost and infrastructure are critical. Fine-tuning larger models requires more computational resources, which increases expenses. If your dataset is small or you’re prototyping, start with a smaller model to validate results before scaling. Also, evaluate the model’s base performance: if it already performs well on your task without fine-tuning, minor adjustments (like prompt engineering) might suffice. Finally, test multiple models if possible. For instance, fine-tune both Cohere Command and Titan Text on a subset of your data to compare accuracy, latency, and cost-effectiveness before committing to one. Always prioritize models with clear documentation and community support to streamline troubleshooting.