AWS bedrock documentation change
Summary
Restructured model customization methods section into a table format, removed 'Continued pre-training' method, updated section title to include 'security', and reordered method descriptions
Security assessment
The changes reorganize content but don't address vulnerabilities or security incidents. The title update to include 'security' doesn't add new security documentation, and no security weaknesses or incidents are mentioned in the diff. The removal of 'Continued pre-training' method doesn't indicate security concerns.
Diff
diff --git a/bedrock/latest/userguide/custom-models.md b/bedrock/latest/userguide/custom-models.md index 1990e57db..d48523841 100644 --- a//bedrock/latest/userguide/custom-models.md +++ b//bedrock/latest/userguide/custom-models.md @@ -5 +5 @@ -Guidelines for model customization +Model customization methodsGuidelines for model customization @@ -9,27 +9 @@ Guidelines for model customization -Model customization is the process of providing training data to a model in order to improve its performance for specific use-cases. You can customize Amazon Bedrock foundation models in order to improve their performance and create a better customer experience. Amazon Bedrock currently provides the following customization methods. - - * **Distillation** - -Use distillation to transfer knowledge from a larger more intelligent model (known as teacher) to a smaller, faster, and cost-efficient model (known as student). Amazon Bedrock automates the distillation process by using the latest data synthesis techniques to generate diverse, high-quality responses from the teacher model, and fine-tunes the student model. - -To use distillation, you select a teacher model whose accuracy you want to achieve for your use case, and a student model to fine-tune. Then, you provide use case-specific prompts as input data. Amazon Bedrock generates responses from the teacher model for the given prompts, and then uses the responses to fine-tune the student model. You can optionally provide labeled input data as prompt-response pairs. - -For more information about using distillation see [Customize a model with distillation in Amazon Bedrock](./model-distillation.html). - - * **Reinforcement fine-tuning** - -Reinforcement fine-tuning improves foundation model alignment with your specific use case through feedback-based learning. Instead of providing labeled input-output pairs, you define reward functions that evaluate response quality. The model learns iteratively by receiving feedback scores from these reward functions. - -You can use existing Bedrock invocation logs as training data or upload custom prompt datasets. You can define reward functions using AWS Lambda to evaluate response quality. Amazon Bedrock automates the training workflow and provides real-time metrics to monitor model learning progress. - -For more information about using reinforcement fine-tuning, see [Customize a model with reinforcement fine-tuning in Amazon Bedrock](./reinforcement-fine-tuning.html). - - * **Supervised fine-tuning** - -Provide _labeled_ data in order to train a model to improve performance on specific tasks. By providing a training dataset of labeled examples, the model learns to associate what types of outputs should be generated for certain types of inputs. The model parameters are adjusted in the process and the model's performance is improved for the tasks represented by the training dataset. - - * **Continued pre-training** - -Provide _unlabeled_ data to pre-train a foundation model by familiarizing it with certain types of inputs. You can provide data from specific topics in order to expose a model to those areas. The Continued Pre-training process will tweak the model parameters to accommodate the input data and improve its domain knowledge. - -For example, you can train a model with private data, such as business documents, that are not publicly available for training large language models. Additionally, you can continue to improve the model by retraining the model with more unlabeled data as it becomes available. +Model customization is the process of providing training data to a model in order to improve its performance for specific use-cases. You can customize Amazon Bedrock foundation models in order to improve their performance and create a better customer experience. @@ -36,0 +11 @@ For example, you can train a model with private data, such as business documents +## Model customization methods @@ -37,0 +13 @@ For example, you can train a model with private data, such as business documents +Amazon Bedrock currently provides the following customization methods. @@ -38,0 +15,5 @@ For example, you can train a model with private data, such as business documents +Customization method | Description | How to use | For more information +---|---|---|--- +Supervised fine-tuning | Provide _labeled_ data in order to train a model to improve performance on specific tasks. By providing a training dataset of labeled examples, the model learns to associate what types of outputs should be generated for certain types of inputs. The model parameters are adjusted in the process and the model's performance is improved for the tasks represented by the training dataset. | Create a training dataset of labeled input-output pairs that represent the specific tasks you want the model to improve on. Submit a fine-tuning job with your labeled dataset. | [Customize a model with fine-tuning in Amazon Bedrock](./custom-model-fine-tuning.html) +Reinforcement fine-tuning | Improves foundation model alignment with your specific use case through feedback-based learning. Instead of providing labeled input-output pairs, you define reward functions that evaluate response quality. The model learns iteratively by receiving feedback scores from these reward functions. | Use existing Bedrock invocation logs as training data or upload custom prompt datasets. Define reward functions using AWS Lambda to evaluate response quality. Amazon Bedrock automates the training workflow and provides real-time metrics to monitor model learning progress. | [Customize a model with reinforcement fine-tuning in Amazon Bedrock](./reinforcement-fine-tuning.html) +Distillation | Transfer knowledge from a larger more intelligent model (known as teacher) to a smaller, faster, and cost-efficient model (known as student). Amazon Bedrock automates the distillation process by using the latest data synthesis techniques to generate diverse, high-quality responses from the teacher model, and fine-tunes the student model. | Select a teacher model whose accuracy you want to achieve for your use case, and a student model to fine-tune. Then, provide use case-specific prompts as input data. Amazon Bedrock generates responses from the teacher model for the given prompts, and then uses the responses to fine-tune the student model. You can optionally provide labeled input data as prompt-response pairs. | [Customize a model with distillation in Amazon Bedrock](./model-distillation.html) @@ -60 +41 @@ Tool use -Supervised fine-tuning +Model customization access and security