Fine Tuning VILA1.5 With New Dataset

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Introduction

Fine-tuning pre-trained language models has become a crucial step in natural language processing (NLP) tasks. VILA1.5, a variant of the VILA model, has shown promising results in various NLP tasks. However, fine-tuning this model with a custom labeled dataset can be a challenging task, especially when there are no clear guidelines available. In this article, we will provide a step-by-step guide on fine-tuning VILA1.5 with a new dataset.

System Requirements

Before we dive into the fine-tuning process, let's ensure that our system meets the minimum requirements. We will be using a well-resourced cluster with 2 A100 GPUs and 100GB RAM on a Debian GNU/Linux 11 operating system and Linux 5.10.0-34-amd64 kernel.

Cloning the Repository and Setting Up the Environment

To fine-tune VILA1.5, we need to clone the repository and set up the environment. We can follow the same steps as we did for NVILA-Lite-2B, which are described in the GitHub repository here. We will run the environment_setup.sh script to set up the environment.

Preparing the Dataset

Once we have set up the environment, we need to prepare our custom labeled dataset. We will need to create a new dataset configuration file and update the default.yaml file in the llava/data/registry/datasets directory.

Configuring the Scripts

The scripts sft.sh and 3_sft.sh are quite different, and we need to configure them to match our system and dataset. We will need to update the data_mixture variable in the 3_sft.sh script to point to our custom dataset.

Model Stage 2 Checkpoint

To fine-tune VILA1.5, we need a model stage 2 checkpoint. Unfortunately, we cannot find a checkpoint for VILA1.5. However, we can try using the NVILA-Lite-8B-stage2 checkpoint as a substitute.

Adjusting the Configuration

We will need to adjust the configuration to match our system and dataset. We will update the default.yaml file in the llava/data/registry/datasets directory to point to our custom dataset.

Running the Fine-Tuning Script

Once we have configured the scripts and updated the configuration files, we can run the fine-tuning script. We will use the 3_sft.sh script to fine-tune VILA1.5 with our custom dataset.

Troubleshooting

If we encounter any issues during the fine-tuning process, we can refer to the troubleshooting section of the GitHub repository.

Conclusion

Fine-tuning VILA1.5 with a new dataset can be a challenging task, but with the right guidance, we can overcome the obstacles. In this article, we have provided a step-by-step guide on fine-tuning VILA1.5 with a new dataset. We have covered the system requirements, cloning the repository and setting up the, preparing the dataset, configuring the scripts, and running the fine-tuning script. We hope that this guide will be helpful to those who are trying to fine-tune VILA1.5 with a new dataset.

System Requirements

  • Debian GNU/Linux 11 operating system
  • Linux 5.10.0-34-amd64 kernel
  • 2 A100 GPUs
  • 100GB RAM

Cloning the Repository and Setting Up the Environment

  • Clone the VILA repository from GitHub
  • Run the environment_setup.sh script to set up the environment

Preparing the Dataset

  • Create a new dataset configuration file
  • Update the default.yaml file in the llava/data/registry/datasets directory

Configuring the Scripts

  • Update the data_mixture variable in the 3_sft.sh script to point to our custom dataset

Model Stage 2 Checkpoint

  • Use the NVILA-Lite-8B-stage2 checkpoint as a substitute

Adjusting the Configuration

  • Update the default.yaml file in the llava/data/registry/datasets directory to point to our custom dataset

Running the Fine-Tuning Script

  • Run the 3_sft.sh script to fine-tune VILA1.5 with our custom dataset

Troubleshooting

  • Refer to the troubleshooting section of the GitHub repository for any issues encountered during the fine-tuning process.
    Fine Tuning VILA1.5 with a New Dataset: A Q&A Guide ===========================================================

Introduction

Fine-tuning pre-trained language models has become a crucial step in natural language processing (NLP) tasks. VILA1.5, a variant of the VILA model, has shown promising results in various NLP tasks. However, fine-tuning this model with a custom labeled dataset can be a challenging task, especially when there are no clear guidelines available. In this article, we will provide a Q&A guide on fine-tuning VILA1.5 with a new dataset.

Q: What are the system requirements for fine-tuning VILA1.5?

A: The system requirements for fine-tuning VILA1.5 are:

  • Debian GNU/Linux 11 operating system
  • Linux 5.10.0-34-amd64 kernel
  • 2 A100 GPUs
  • 100GB RAM

Q: How do I clone the VILA repository and set up the environment?

A: To clone the VILA repository and set up the environment, follow these steps:

  1. Clone the VILA repository from GitHub
  2. Run the environment_setup.sh script to set up the environment

Q: How do I prepare my custom labeled dataset?

A: To prepare your custom labeled dataset, follow these steps:

  1. Create a new dataset configuration file
  2. Update the default.yaml file in the llava/data/registry/datasets directory

Q: How do I configure the scripts for fine-tuning VILA1.5?

A: To configure the scripts for fine-tuning VILA1.5, follow these steps:

  1. Update the data_mixture variable in the 3_sft.sh script to point to your custom dataset

Q: What is a model stage 2 checkpoint, and how do I obtain one?

A: A model stage 2 checkpoint is a pre-trained model that has been trained on a specific task or dataset. Unfortunately, we cannot find a checkpoint for VILA1.5. However, we can try using the NVILA-Lite-8B-stage2 checkpoint as a substitute.

Q: How do I adjust the configuration for fine-tuning VILA1.5?

A: To adjust the configuration for fine-tuning VILA1.5, follow these steps:

  1. Update the default.yaml file in the llava/data/registry/datasets directory to point to your custom dataset

Q: How do I run the fine-tuning script for VILA1.5?

A: To run the fine-tuning script for VILA1.5, follow these steps:

  1. Run the 3_sft.sh script to fine-tune VILA1.5 with your custom dataset

Q: What if I encounter any issues during the fine-tuning process?

A: If you encounter any issues during the fine-tuning process, refer to the troubleshooting section of the GitHub repository for any issues encountered during the fine-tuning process.

Q: Can I use a different pre-trained model for fine-tuning VILA1.5?

A: Yes, you can use a different pre-trained model for fine-tuning VILA1.5. However, you will need to adjust the configuration and scripts accordingly.

Q: How long does the fine-tuning process take?

A: The fine-tuning process can take several hours or even days, depending on the size of the dataset and the computational resources available.

Q: Can I fine-tune VILA1.5 on a smaller dataset?

A: Yes, you can fine-tune VILA1.5 on a smaller dataset. However, you may need to adjust the configuration and scripts accordingly to ensure that the model is trained effectively.

Conclusion

Fine-tuning VILA1.5 with a new dataset can be a challenging task, but with the right guidance, we can overcome the obstacles. In this article, we have provided a Q&A guide on fine-tuning VILA1.5 with a new dataset. We hope that this guide will be helpful to those who are trying to fine-tune VILA1.5 with a new dataset.

System Requirements

  • Debian GNU/Linux 11 operating system
  • Linux 5.10.0-34-amd64 kernel
  • 2 A100 GPUs
  • 100GB RAM

Cloning the Repository and Setting Up the Environment

  • Clone the VILA repository from GitHub
  • Run the environment_setup.sh script to set up the environment

Preparing the Dataset

  • Create a new dataset configuration file
  • Update the default.yaml file in the llava/data/registry/datasets directory

Configuring the Scripts

  • Update the data_mixture variable in the 3_sft.sh script to point to your custom dataset

Model Stage 2 Checkpoint

  • Use the NVILA-Lite-8B-stage2 checkpoint as a substitute

Adjusting the Configuration

  • Update the default.yaml file in the llava/data/registry/datasets directory to point to your custom dataset

Running the Fine-Tuning Script

  • Run the 3_sft.sh script to fine-tune VILA1.5 with your custom dataset

Troubleshooting

  • Refer to the troubleshooting section of the GitHub repository for any issues encountered during the fine-tuning process.