Check the CUDA configuration

Oct 13, 2025 by wbrum.org 61 views

Introduction

Finetuning a pre-trained model is a crucial step in many natural language processing (NLP) tasks. In this article, we will discuss how to finetune a DeepSeekVL model, a state-of-the-art model for visual language understanding. We will cover the data format definition, code execution, and common errors that may occur during the finetuning process.

Data Format Definition

Before we dive into the finetuning process, let's first discuss the data format definition. The DeepSeekVL model requires a specific data format to function properly. The data format should be in JSONL (JSON Lines) format, where each line represents a single sample.

JSONL Data Format

The JSONL data format is a simple and efficient way to store data in JSON format, one line per sample. Each line should contain the following keys:

query: The input query or prompt.
response: The expected response or answer.
image: A list of image paths or URLs.

Here's an example of what the data format should look like:

{"query": "<image_placeholder>描述一下机器人在做什么", "response": "拿东西", "image": ["demo/100.png"]}

In this example, the query is a prompt that describes an image, the response is the expected answer, and the image is a list containing a single image path.

Code Execution

Now that we have defined the data format, let's discuss the code execution. The code execution involves running the DeepSeekVL model using the Swift framework. Here's an example of how to run the code:

NPROC_PER_NODE=4 CUDA_VISIBLE_DEVICES=0,1,2,3 swift sft \
    --model ./deepseek/ \
    --dataset ./test.jsonl \
    --train_dataset_sample -1 \
    --sft_type full \
    --deepspeed default-zero2

This code executes the DeepSeekVL model using the Swift framework, with the following parameters:

NPROC_PER_NODE: The number of processes to run per node.
CUDA_VISIBLE_DEVICES: The list of CUDA devices to use.
swift sft: The Swift framework command to run the model.
--model: The path to the pre-trained model.
--dataset: The path to the dataset file.
--train_dataset_sample: The number of samples to use for training.
--sft_type: The type of SFT (Sparse Fourier Transform) to use.
--deepspeed: The DeepSpeed configuration to use.

Common Errors

When running the code, you may encounter common errors such as:

JSONDecodeError: This error occurs when the JSONL data format is not correctly defined. Make sure that each line in the dataset file contains the required keys (query, response, and image).
CUDAError: This error occurs when the CUDA devices are not properly configured. Make sure that the CUDA_VISIBLE_DEVICES environment variable is set correctly.

Troubleshooting

If you encounter any errors during the finetuning process, here are some troubleshooting steps you can take:

Check the data format: Make sure that the data format is correctly defined and that each line in the dataset file contains the required keys.
Check the code execution: Make sure that the code execution is correct and that the parameters are properly set.
Check the CUDA configuration: Make sure that the CUDA devices are properly configured and that the CUDA_VISIBLE_DEVICES environment variable is set correctly.

Conclusion

Finetuning a DeepSeekVL model is a crucial step in many NLP tasks. By following the data format definition and code execution guidelines outlined in this article, you should be able to successfully finetune the model. Remember to troubleshoot any errors that may occur during the finetuning process by checking the data format, code execution, and CUDA configuration.

Additional Resources

For more information on the DeepSeekVL model and the Swift framework, please refer to the following resources:

DeepSeekVL Model: https://github.com/deepseekvl/deepseekvl
Swift Framework: https://github.com/swift/swift

Introduction

Finetuning a DeepSeekVL model is a crucial step in many natural language processing (NLP) tasks. However, it can be a complex process, and many users may have questions about how to finetune the model. In this article, we will answer some of the most frequently asked questions about finetuning a DeepSeekVL model.

Q: What is the data format required for finetuning a DeepSeekVL model?

A: The data format required for finetuning a DeepSeekVL model is a JSONL (JSON Lines) format, where each line represents a single sample. Each line should contain the following keys:

query: The input query or prompt.
response: The expected response or answer.
image: A list of image paths or URLs.

Here's an example of what the data format should look like:

{"query": "<image_placeholder>描述一下机器人在做什么", "response": "拿东西", "image": ["demo/100.png"]}

Q: How do I configure the CUDA devices for finetuning a DeepSeekVL model?

A: To configure the CUDA devices for finetuning a DeepSeekVL model, you need to set the CUDA_VISIBLE_DEVICES environment variable. This variable specifies the list of CUDA devices to use. For example:

export CUDA_VISIBLE_DEVICES=0,1,2,3

This will use the first four CUDA devices (0, 1, 2, and 3) for finetuning the model.

Q: What is the difference between `--sft_type full` and `--sft_type sparse`?

A: The --sft_type parameter specifies the type of SFT (Sparse Fourier Transform) to use for finetuning the model. The full type uses the full SFT, while the sparse type uses a sparse SFT. The full type is generally faster but uses more memory, while the sparse type is generally slower but uses less memory.

Q: How do I troubleshoot errors during finetuning a DeepSeekVL model?

A: To troubleshoot errors during finetuning a DeepSeekVL model, you can try the following steps:

Check the data format: Make sure that the data format is correctly defined and that each line in the dataset file contains the required keys.
Check the code execution: Make sure that the code execution is correct and that the parameters are properly set.
Check the CUDA configuration: Make sure that the CUDA devices are properly configured and that the CUDA_VISIBLE_DEVICES environment variable is set correctly.

Q: Can I use a different pre-trained model for finetuning a DeepSeekVL model?

A: Yes, you can use a different pre-trained model for finetuning a DeepSeekVL model. However, you need to make sure that the pre-trained model is compatible with the DeepSeekVL model architecture. You can use the --model parameter to specify the path to the pre-trained model.

Q: How do I evaluate the performance of a finetuned DeepSeekVL model?

A: To evaluate the performance of a finetuned DeepSeekVL model, you can use standard evaluation metrics such as accuracy, precision, recall, and F1-score. You can also use more advanced evaluation metrics such as ROUGE score and BLEU score.

Conclusion

Finetuning a DeepSeekVL model is a crucial step in many NLP tasks. By following the guidelines outlined in this article and answering the frequently asked questions, you should be able to successfully finetune a DeepSeekVL model and achieve state-of-the-art results in your NLP tasks.

Additional Resources

For more information on the DeepSeekVL model and the Swift framework, please refer to the following resources:

DeepSeekVL Model: https://github.com/deepseekvl/deepseekvl
Swift Framework: https://github.com/swift/swift

By following the guidelines outlined in this article and referring to the additional resources, you should be able to successfully finetune a DeepSeekVL model and achieve state-of-the-art results in your NLP tasks.