Supercharge GPT-J: Fine Tune with Amazon Sagemaker

Table of Contents

1. Introduction
2. Understanding Amazon Sagemaker Jumpstart
3. Fine-Tuning a Language Text Generation Model
   • 3.1 The Financial Domain
   • 3.2 The Hugging Phase Transformer Model
4. Running Inference on the Model
   • 4.1 Baseline Inference Results
   • 4.2 Custom Data Set and Fine-Tuning
5. Retrieving Training Artifacts
   • 5.1 Training on a GPU Instance
   • 5.2 Training Parameters and Hyperparameters
6. The Deep Speed Library
   • 6.1 Integrating Deep Speed with Sagemaker Hugging Phase DLC
   • 6.2 Benefits of Deep Speed
7. Training and Fine-Tuning
   • 7.1 Fine-Tuning Process
   • 7.2 Training and Evaluation Loss
8. Deploying the Model
   • 8.1 GPU Instance for Deployment
   • 8.2 Comparison of Inference Results
9. Model Evaluation
   • 9.1 Perplexity as a Model Evaluation Metric
   • 9.2 Improvements in Perplexity
10. Conclusion

Introduction

In this article, we will explore how to use Amazon Sagemaker Jumpstart to fine-tune a large language text generation model on a domain-specific dataset. We will specifically focus on the financial domain and use the Hugging Phase Transformer model for fine-tuning. We will discuss the process of running inference on the model, retrieving training artifacts, and deploying the model. Additionally, we will evaluate the model performance using perplexity as a metric.

Understanding Amazon Sagemaker Jumpstart

Amazon Sagemaker Jumpstart provides a streamlined approach to fine-tuning models on domain-specific datasets. It leverages the capabilities of the Sagemaker Hugging Phase Deep Learning Container (DLC) and the Deep Speed library. Deep Speed reduces computing power and memory usage while enabling large distributed models to be trained more efficiently on existing hardware. With Jumpstart, You can easily fine-tune models without having to manually configure all the parameters.

Fine-Tuning a Language Text Generation Model

3.1 The Financial Domain

For our example, we will focus on the financial domain. We will use publicly available SEC filings of Amazon from 2021 and 2022 as our domain-specific dataset. The goal is to fine-tune the model to generate insightful data related to the financial domain.

3.2 The Hugging Phase Transformer Model

The Hugging Phase Transformer model is a powerful Transformer model used for text generation tasks. It generates text Based on a given prompt. In our case, we will run inference on the model without any fine-tuning and then compare the results before and after fine-tuning.

Running Inference on the Model

To understand the baseline performance of the model, we will run inference on the unmodified model. We will provide three example Prompts to the model and analyze the generated text. The responses will give us insights into the model's initial capabilities.

4.1 Baseline Inference Results

The baseline inference results Show that the unmodified model generates text related to the form 10K report, Amazon's directors, and other miscellaneous details. However, the generated text lacks cohesiveness and relevance to the financial domain.

4.2 Custom Data Set and Fine-Tuning

To improve the model's performance in the financial domain, we will fine-tune it on a custom dataset. The sample text file used for fine-tuning will include SEC filings specific to Amazon. By fine-tuning the model on this dataset, we expect it to generate more accurate and insightful text related to the financial domain.

Retrieving Training Artifacts

Before we can begin the fine-tuning process, we need to retrieve the necessary training artifacts. This includes the Docker container for training, the pre-trained model, and the training hyperparameters. We will also set the training parameters such as the S3 paths for training and validation data, output bucket for storing artifacts, and define the hyperparameters.

5.1 Training on a GPU Instance

We will perform the fine-tuning process on a GPU instance to leverage its computational power. The training image for the specific model will be retrieved, and the latest version will be used for training. This ensures that We Are working with the most up-to-date resources.

5.2 Training Parameters and Hyperparameters

Training parameters such as the device train batch size will be set, and we will perform automatic model tuning, also known as hyperparameter tuning. This allows the model to find the optimal set of hyperparameters for training. We will define the objective metric to minimize loss and set the learning rate and scaling Type. Additionally, we will define the max drop and max Parallel job values.

The Deep Speed Library

The Deep Speed library plays a crucial role in optimizing the training process. It reduces computing power and memory usage while enabling better parallelism in training large distributed models. It seamlessly integrates with Sagemaker Hugging Phase DLC, allowing for efficient model training without manual parameter configuration.

6.1 Integrating Deep Speed with Sagemaker Hugging Phase DLC

By integrating Deep Speed with Sagemaker Hugging Phase DLC, we can leverage its benefits in the background. This includes single-node distributed training, gradient checkpoints, and model parallelism. The integration simplifies the fine-tuning process and allows for efficient training on domain-specific datasets.

6.2 Benefits of Deep Speed

Deep Speed offers several benefits for training large models. It optimizes computing power and memory usage, resulting in faster training times. The library facilitates parallelism, enabling efficient training on existing hardware. These benefits make Deep Speed a valuable tool for fine-tuning models on domain-specific datasets.

Training and Fine-Tuning

The fine-tuning process involves training the model on the custom dataset and evaluating its performance. We will analyze the training and evaluation losses to understand the model's progress during the training process.

7.1 Fine-Tuning Process

During the fine-tuning process, the model adjusts its weights and parameters to become more attuned to the financial domain. This helps the model generate more accurate and Relevant text related to financial topics.

7.2 Training and Evaluation Loss

The training and evaluation loss metrics provide insights into how the model is learning and improving during the fine-tuning process. By analyzing these metrics, we can assess the model's performance and make any necessary adjustments to achieve better results.

Deploying the Model

Once the model has been fine-tuned, we can deploy it for real-time inference. We will utilize a GPU instance similar to the one used for training. By deploying the model, we can generate text based on prompt inputs and assess its performance in generating relevant and accurate information related to the financial domain.

8.1 GPU Instance for Deployment

To ensure optimal performance during deployment, we will use a GPU instance, specifically the G5 12x large instance. This instance type provides the necessary computational power for efficient and fast inference.

8.2 Comparison of Inference Results

After deploying the fine-tuned model, we will compare the inference results with the baseline results. By analyzing the generated text, we can assess the model's performance improvement in generating insightful data for the financial domain.

Model Evaluation

To evaluate the fine-tuned model, we will utilize the perplexity metric. Perplexity measures the model's uncertainty in predicting the next word given the Context. Lower perplexity values indicate better model performance.

9.1 Perplexity as a Model Evaluation Metric

Perplexity helps us understand how well the fine-tuned model grasps the context and language structure. It quantifies the model's ability to predict the next word accurately. By calculating perplexity, we can assess the model's overall performance and compare it to the baseline model.

9.2 Improvements in Perplexity

By comparing the perplexity values before and after fine-tuning, we can observe the improvements in the fine-tuned model. Lower perplexity values indicate that the model has a better understanding of the financial domain and can generate more accurate and relevant text.

Conclusion

In conclusion, Amazon Sagemaker Jumpstart provides a powerful and streamlined approach to fine-tuning language text generation models on domain-specific datasets. By leveraging the capabilities of the Hugging Phase Transformer model and the Deep Speed library, we can achieve significant improvements in model performance. Through the fine-tuning process, the model becomes more attuned to the financial domain, resulting in the generation of insightful and accurate text.