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Deploying LLaMA on Amazon SageMaker and Fine-Tuning with Custom Datasets

IXC Digital •
#llm#llama#aws

Introduction

Fine-tuning large language models (LLMs) is essential to tailor them for specific domains such as finance, medicine, or IT. Amazon SageMaker provides a powerful environment to fine-tune pre-trained models, such as Meta Llama 2 7B, to improve their domain-specific performance. In this blog, we will walk through the process of fine-tuning a model in Amazon SageMaker, deploying it, and evaluating its performance.

Large Language Models (LLMs) like Meta’s LLaMA offer incredible capabilities for text generation, summarization, and more. Deploying LLaMA on AWS SageMaker allows scalable inference and fine-tuning with your own dataset. In this guide, we will go through the steps to deploy LLaMA on SageMaker and fine-tune it using a custom dataset.

Prerequisites

Before getting started, ensure you have the following:

Step 1: Set Up Your Environment

Before beginning the fine-tuning process, ensure you have an AWS account with access to SageMaker. You will also need a temporary AWS user account with limited permissions if you are using Udacity’s Cloud Lab.

Install Required Packages

First, install the necessary Python packages in your SageMaker notebook:

!pip install --upgrade sagemaker datasets

Step 2: Select the Pre-Trained Model

For this project, we will fine-tune the Meta Llama 2 7B model. Define the model ID and version as follows:

model_id, model_version = "meta-textgeneration-llama-2-7b", "2.*"

Step 3: Choose Your Training Dataset

Select a dataset relevant to your domain. The dataset should be stored in an AWS S3 bucket.

Step 4: Configure the Fine-Tuning Process

Create an instance of the JumpStartEstimator and set hyperparameters for training:

from sagemaker.jumpstart.estimator import JumpStartEstimator

estimator = JumpStartEstimator(
    model_id=model_id,
    environment={"accept_eula": "true"},
    instance_type="ml.g5.2xlarge"
)

estimator.set_hyperparameters(instruction_tuned="False", epoch="5")

Start the fine-tuning process by specifying the dataset:

estimator.fit({"training": "s3://genaiwithawsproject2024/training-datasets/your_chosen_domain"})

Step 5: Deploy the Fine-Tuned Model

Once training is complete, deploy the model for inference:

finetuned_predictor = estimator.deploy(instance_type="ml.g5.2xlarge", initial_instance_count=1)

Step 6: Evaluate the Model

Use domain-specific test inputs to evaluate the fine-tuned model. Define a function to process and print the model’s responses:

def print_response(payload, response):
    print(payload["inputs"])
    print(f"> {response}")
    print("\n==================================\n")

Run inference on test data:

payload = {
    "inputs": "Your domain-specific test input",
    "parameters": {
        "max_new_tokens": 64,
        "top_p": 0.9,
        "temperature": 0.6,
        "return_full_text": False,
    },
}

response = finetuned_predictor.predict(payload, custom_attributes="accept_eula=true")
print_response(payload, response)

Compare the responses of the fine-tuned model with the pre-trained model to assess improvements in domain-specific knowledge.

Step 7: Delete the Model Deployment

To avoid unnecessary AWS charges, delete the deployed model and endpoint after evaluation:

finetuned_predictor.delete_model()
finetuned_predictor.delete_endpoint()

Conclusion

Fine-tuning a language model in Amazon SageMaker allows you to customize it for specific business needs. This process involves selecting a pre-trained model, choosing a relevant dataset, configuring and training the model, deploying it, and evaluating its performance. By leveraging AWS SageMaker, you can efficiently build domain-specific AI models that enhance decision-making and automation in various industries.

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