MiniCPM-V Finetuning

We offer the official scripts for easy finetuning of the pretrained MiniCPM-Llama3-V 2.5 and MiniCPM-V 2.0 on downstream tasks. Our finetune scripts use transformers Trainer and DeepSpeed by default.

Data preparation

To prepare your finetuning data, you should formulate each sample as a dictionary consisting of an id, an image path list with an image, and a list of conversations. Then save data samples in JSON files.

For the vision-language example with image, you are required to provide <image> to define the position to insert the image embeddings. If you don't provide <image>, the image will be placed at the front of the conversation.

vision-language example (vl_finetune_data.json) with 1 samples.

  [
    {
      "id": "0",
      "image": 'path/to/image_0.jpg',
      "conversations": [
            {
              'role': 'user', 
              'content': '<image>\nHow many desserts are on the white plate?'
            }, 
            {
                'role': 'assistant', 
                'content': 'There are three desserts on the white plate.'
            },   
            {
                'role': 'user', 
                'content': 'What type of desserts are they?'
            },
            {
                'role': 'assistant', 
                'content': 'The desserts are cakes with bananas and pecans on top. They share similarities with donuts, but the presence of bananas and pecans differentiates them.'
            }, 
            {
                'role': 'user', 
                'content': 'What is the setting of the image?'}, 
            {
                'role': 'assistant', 
                'content': 'The image is set on a table top with a plate containing the three desserts.'
            },
        ]
    },
  ]

Full-parameter finetuning

Full-parameter parameter finetuning requires updating all parameters of LLM in the whole training process. Please specify the correct MODEL path, DATA path and LLM_TYPE in the shell scripts.

MODEL="openbmb/MiniCPM-Llama3-V-2_5" # or openbmb/MiniCPM-V-2
DATA="path/to/trainging_data" # json file
EVAL_DATA="path/to/test_data" # json file
LLM_TYPE="llama3" # if use openbmb/MiniCPM-V-2, please set LLM_TYPE=minicpm

To launch your training, run the following script:

sh finetune_ds.sh

Specially, Llama3 has a different chat_template for training and inference, we modified the chat_template for training, so please take care to restore the chat_template when inference on the training ckpt.

LoRA finetuning

The LoRA allows light-weight model tuning with only a small subset of parameters updated. We provide the LoRA implementation based on peft. To launch your training, run the following script:

sh finetune_ds_lora.sh

After training, you could load the model with the path to the adapter. We advise you to use absolute path for your pretrained model. This is because LoRA only saves the adapter and the absolute path in the adapter configuration json file is used for finding out the pretrained model to load.

from peft import AutoPeftModelForCausalLM

model = AutoPeftModelForCausalLM.from_pretrained(
    # path to the output directory
    path_to_adapter,
    device_map="auto",
    trust_remote_code=True
).eval()

Model Fine-tuning Memory Usage Statistics

The following table presents the memory usage of the model when fine-tuning using NVIDIA A100 (80GiB) GPUs under different numbers of GPUs. The fine-tuning was performed with the DeepSpeed Zero-2 optimization and Gradient Checkpointing techniques, with a maximum length set to 2048 and batch size set to 1.

Fine-tuning Method	GPUs: 2	GPUs: 4	GPUs: 8
LoRA Fine-tuning	31.2 GiB	29.3 GiB	28.4GiB
Full Parameters Fine-tuning	Out of memory	75.0 GiB	51.2GiB

Notes

Fine-tuning Method: Displays two different fine-tuning strategies, LoRA fine-tuning and Full parameters fine-tuning.
Number of GPUs: The table lists the memory usage for configurations with 2, 4, and 8 GPUs.
Memory Usage: Expressed in GiB, this shows the required memory for each fine-tuning method under corresponding GPU configurations.
Out of memory: Indicates that the memory was insufficient for full parameters fine-tuning under the current GPU configurations.

Finetuning FAQs

Q: How do I use the `flash_attention_2` implementation when loading a pretrained model?

A: If your environment supports flash_attn2, you can add an argument _attn_implementation="flash_attention_2" when using the AutoModel.from_pretrained method to load a model. For example:

model = AutoModel.from_pretrained('model_name', _attn_implementation="flash_attention_2")

Q: What if our data is resized to 512? Can we use the original image size instead?

A: Our model supports up to 1344x1344 lossless encoding. If you are currently resizing your images to 512, you might want to try using the original image sizes instead. Our system automatically includes a high-definition image encoding scheme by default.

Q: What should we do if we encounter out-of-memory (OOM) errors?

A: If you experience OOM issues, consider reducing the batch size (bs). To maintain an equivalent total batch size, you can adjust the gradient_accumulation_steps setting. This approach allows you to manage memory usage effectively while still processing the desired amount of data per training step.

Q: How can we determine the maximum length for our training data, and what if we do not want to train the vision encoder?

A: I recommend using this function here to sample the length of your training data. Note that the input_ids length includes the image portion. Once you determine the maximum length, you can specify it in the startup command using --model_max_length xxx.

Additionally, if you prefer not to train the vision encoder, you can add --tune_vision false to your command.

Q: How can we adjust training hyperparameters when using LoRA to train our model?

A: You can refer to the LoRA documentation for guidance on adjusting your training hyperparameters when using LoRA. This documentation provides detailed information on configuring various parameters specific to the LoRA adaptation technique.

Customizing Hyperparameters

To tailor the training process according to your specific requirements, you can adjust various hyperparameters. For comprehensive documentation on available hyperparameters and their functionalities, you can refer to the official Transformers documentation and Lora documentation. Experimentation and fine-tuning of these parameters are essential for achieving optimal model performance tailored to your specific task and dataset.