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Introduction
Generative artificial intelligence (AI) models have become increasingly popular and powerful, enabling a wide range of applications such as text generation, summarization, question answering, and code generation. However, despite their impressive capabilities, these models often struggle with domain-specific tasks or use cases due to their general training data. To address this challenge, fine-tuning these models on specific data is crucial for achieving optimal performance in specialized domains.
Meta Llama 3 Overview
Meta Llama 3 comes in two parameter sizes—8B and 70B with 8,000 context length—that can support a broad range of use cases with improvements in reasoning, code generation, and instruction following. Meta Llama 3 uses a decoder-only transformer architecture and new tokenizer that provides improved model performance with 128,000 context size. In addition, Meta improved post-training procedures that substantially reduced false refusal rates, improved alignment, and increased diversity in model responses.
SageMaker JumpStart
SageMaker JumpStart is a powerful feature within the SageMaker machine learning (ML) environment that provides ML practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs). With this managed service, ML practitioners get access to a growing list of cutting-edge models from leading model hubs and providers that they can deploy to dedicated SageMaker instances within a network isolated environment, and customize models using SageMaker for model training and deployment.
Fine-tuning Meta Llama 3 Models
No-code Fine-tuning through the SageMaker Studio UI
SageMaker JumpStart provides access to publicly available and proprietary foundation models from third-party and proprietary providers. Data scientists and developers can quickly prototype and experiment with various ML use cases, accelerating the development and deployment of ML applications.
Step 1: Access Meta Llama 3 FMs
- In SageMaker Studio, navigate to the JumpStart view
- Choose the Meta provider
- Select the Meta-Llama-3-8B-Instruct model
- View model details, train, deploy, optimize, and evaluate the model
Step 2: Fine-tune the Model
- Point to the Amazon S3 bucket containing the training and validation datasets for fine-tuning
- Configure deployment configuration, hyperparameters, and security settings for fine-tuning
- Choose Submit to start the training job on a SageMaker ML instance
Fine-tuning using the SageMaker Python SDK
You can also fine-tune Meta Llama 3 models using the SageMaker Python SDK. A sample notebook with the full instructions can be found on GitHub.
Example Code
import os
import boto3
from sagemaker.session import Session
from sagemaker.jumpstart.estimator import JumpStartEstimator
# To fine-tune the Llama 3 70B model available on JumpStart, please change model_id to `meta-textgeneration-llama-3-70b`.
model_id = "meta-textgeneration-lllama-3-8b"
accept_eula = "true"
estimator = JumpStartEstimator(
model_id=model_id, environment={"accept_eula": accept_eula}
)
# By default, instruction tuning is set to false. Thus, to use instruction tuning dataset you use instruction_tuned="True"
estimator.set_hyperparameters(instruction_tuned="True", epoch="5")
estimator.fit({"training": train_data_location})
# Deploy the fine-tuned model
finetuned_predictor = estimator.deploy(instance_type="ml.g5.12xlarge")Dataset Formatting
We currently offer two types of fine-tuning: instruction fine-tuning and domain adaption fine-tuning. You can switch to one of the training methods by specifying the parameter instruction_tuned as True or False.
Domain Adaption Format
The text generation model can be fine-tuned on any domain-specific dataset to incorporate domain-specific knowledge and language patterns. After fine-tuning on the domain-specific dataset, the model is expected to generate more relevant and accurate text within that domain.
Instruction Fine-tuning
The text generation model can be instruction-tuned on any text data provided that the data is in the expected format. The instruction-tuned model can be further deployed for inference.
Conclusion
In conclusion, fine-tuning the Meta Llama 3 models on specific data is crucial for achieving optimal performance in specialized domains. This article demonstrated how to fine-tune the Meta Llama 3 models using SageMaker JumpStart, both through the SageMaker Studio UI and the SageMaker Python SDK. Additionally, this article provided examples of dataset formatting and licensing information.
Frequently Asked Questions
Q1: What is Meta Llama 3?
A1: Meta Llama 3 is a set of large language models (LLMs) developed by Meta AI, designed to support a broad range of use cases with improvements in reasoning, code generation, and instruction following.
Q2: What is SageMaker JumpStart?
A2: SageMaker JumpStart is a feature within the SageMaker machine learning (ML) environment that provides ML practitioners access to publicly available and proprietary foundation models (FMs) from leading model hubs and providers.
Q3: How do I fine-tune Meta Llama 3 models using SageMaker JumpStart?
A3: You can fine-tune Meta Llama 3 models using SageMaker JumpStart by accessing the Meta Llama 3 FMs in the SageMaker Studio UI, configuring deployment configuration, hyperparameters, and security settings, and choosing the Submit button to start the training job on a SageMaker ML instance.
Q4: What are the benefits of fine-tuning Meta Llama 3 models?
A4: Fine-tuning Meta Llama 3 models on specific data is crucial for achieving optimal performance in specialized domains, enabling domain-specific knowledge and language patterns, and generating more relevant and accurate text within that domain.
Q5: What are the requirements for fine-tuning Meta Llama 3 models?
A5: To fine-tune Meta Llama 3 models, you need to have an Amazon SageMaker account and access to the Meta Llama 3 FMs in the SageMaker JumpStart environment. Additionally, you need to provide training and validation datasets in the expected format.
