Fine-Tuning a FM

Fine-tuning refers to the process of taking a pretrained foundational model and adjusting its weights and parameters to perform better on a specific task. Instead of the resource-intensive process of training a model from scratch, fine-tuning allows us to leverage the knowledge embedded in a large, pretrained model and adapt it to our own data and requirements.

Amazon Bedrock provides us with access to foundational models from various providers. However, only the following FMs support fine-tuning:

• Amazon Titan Text G1: Express, G1 Lite, and Premier

• Amazon Titan Multimodal Embeddings G1

• Amazon Titan Image Generator G1: V1 and V2

• Anthropic Claude 3: Haiku

• Cohere: Command and Light

• Meta Llama 2: 13B and 70B

• Meta Llama 3.1: 8B Instruct and 70B Instruct

Methods to fine-tune data

Various methods are used to fine-tune foundational models and must be selected based on the application’s requirements, available data, and desired model behavior. Some of the common methods used to fine-tune data are as follows:

• Instruction tuning: In this method, we train our model to respond better to specific prompts or instructions. We do this by exposing our model to a dataset that includes clear instructions to make it more effective at following user commands. This method is particularly useful for creating conversational or customer service chatbots where understanding and following specific prompts is crucial.

• Domain adaptation: This method fine-tunes a model for a specific industry or field, such as legal or financial data. We do this by exposing the model to domain-specific jargon and examples to improve its understanding and ensure it generates relevant responses. This method is useful for specialized applications, such as medical diagnostics or technical document generation.

• Transfer learning: Transfer learning leverages a model’s general understanding by fine-tuning it on new but related tasks. For example, a model pretrained on general text can be fine-tuned for sentiment analysis by focusing on text labeled with sentiment scores. This approach makes the model more effective on specific tasks without having to retrain the model from scratch.

• Continuous pre-training: In continuous pre-training, the model is exposed to new, relevant data over time to maintain or improve its accuracy. This is especially useful in fields that change rapidly, such as news or social media sentiment analysis. Continuous pre-training helps ensure that the model remains updated with recent terminology and trends.

Prepare data for fine-tuning

The outputs given by our model are as good as the training data we provide. Therefore, it is important that we prepare our data before fine-tuning a model. Some things to keep in mind while preparing data for fine-tuning are as follows:

• Data curation: Ensure that the data we provide is of high quality and remove any irrelevant content from the dataset.

• Data governance: While working with sensitive information, make sure to adhere to privacy and compliance requirements

• Dataset size: Ensure that the dataset used is balanced and diverse. A large dataset is generally beneficial, but excessive redundancy can reduce efficiency and increase the risk of overfitting.

• Labeling: Ensure data is accurately labeled, especially for sentiment analysis or classification tasks. This improves model performance by helping it learn the right patterns.

• Representativeness: Include diverse data to capture the range of real-world inputs the model will encounter, reducing potential biases.

• Reinforcement Learning from Human Feedback (RLHF): Incorporate feedback from human reviewers to improve the model’s accuracy on specific tasks through iterative refinement.

Benefits of fine-tuning FMs

Following are some of the main benefits of using fine-tuning:

• Task specialization: By fine-tuning, we can ensure that our models are highly specialized to our specific tasks.

• Improved accuracy: Pretrained models are powerful but generalized for a wide range of tasks. Fine-tuning enhances their accuracy by tailoring them to the specific requirements of our dataset and task. This can achieve significantly better performance than using a generic model.

• Cost and time efficiency: Fine-tuning is faster and less expensive than training a model from scratch.

• Flexibility and adaptability: Fine-tuned models can easily be updated as new data becomes available, keeping the model current with changing trends or new information. Additionally, we can fine-tune models iteratively to improve performance over time.