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Data Organization Guidelines

Your training data should include a CSV file and a video folder structured as follows:

├── example.csv
└── videos/
    ├── example_1_A.mp4
    ├── example_1_B.mp4
    ├── example_2_A.mp4
    ├── example_2_B.mp4
    └── ...

CSV File Format

The CSV file must contain the following columns with exact header names:

  • path_A: Path to video A (e.g., ./videos/example_1_A.mp4).
  • path_B: Path to video B (e.g., ./videos/example_1_B.mp4).
  • prompt: Text description outlining the video content or scene.
  • VQ: Visual quality preference label (possible values: A, B, same).
  • MQ: Motion quality preference label (possible values: A, B, same).
  • TA: Text alignment preference label (possible values: A, B, same).
  • fps_A: Frame rate of video A.
  • num_frames_A: Number of frames in video A.
  • fps_B: Frame rate of video B.
  • num_frames_B: Number of frames in video B.

We provide an example here

Training Script Parameters

The following table summarizes the parameters used in the training script. You can adjust these values as needed for your experiments.

Parameter Description Example Value
--lora_enable Enable LoRA tuning for the model. True
--vision_lora Enable LoRA tuning for the vision component. False
--freeze_vision_tower Freeze the vision tower parameters during training. False
--freeze_llm Freeze the language model parameters during training. False
--tune_merger Enable tuning for the merger module(between the vision encoder and the LLM). True
--fps FPS to sample from the video. 2
--max_frame_pixels Maximum number of pixels per frame allowed. 200704(448*448)
--sample_type Frame sampling strategy. "uniform"
--lora_r LoRA Rank 64
--lora_alpha LoRA Alpha 128
--lora_namespan_exclude Model parts to exclude from LoRA tuning. "['lm_head', 'rm_head', 'embed_tokens']"
--bf16 Use bfloat16 precision training. True
--torch_dtype Torch data type to use during training. "bfloat16"
--num_lora_modules Number of LoRA modules to apply (-1 for all available modules). -1
--model_name_or_path Identifier or path for the pre-trained model. Qwen/Qwen2-VL-2B-Instruct
--meta_data Path to the training metadata CSV file. "./datasets/train/example.csv"
--meta_data_test Path to the valid metadata CSV file. "./datasets/train/example.csv"
--data_dir Directory of the training data. "./datasets/train"
--output_dir Directory where model outputs will be saved. rm_output
--eval_dim Evaluation dimensions (single dimension or multiple dimensions). "VQ" "MQ" "TA"
--output_dim Number of output dimensions. 1
--use_special_tokens Enable the use of special tokens during training. True
--reward_token The token used to indicate reward in the text. "special"
--loss_type Specifies the loss function type. "btt"
--use_tied_data Use tied data for training. True
--prompt_template_type The template type for input template. "detailed_special"
--per_device_train_batch_size Batch size per device for training. 1
--per_device_eval_batch_size Batch size per device for evaluation. 4
--gradient_accumulation_steps Number of gradient accumulation steps. 4
--num_train_epochs Total number of training epochs. 3
--learning_rate Base learning rate for training. 2e-6
--merger_lr Learning rate for the merger module. 2e-6
--vision_lr Learning rate for the vision components. 2e-6
--special_token_lr Learning rate for the special tokens. 2e-6
--report_to Logging backend for training reports. tensorboard
--warmup_ratio Warmup ratio for the learning rate scheduler. 0.05
--lr_scheduler_type Type of learning rate scheduler. "constant_with_warmup"
--eval_strategy Strategy for evaluation during training. "steps"
--logging_epochs Frequency of logging (in terms of epochs). 0.01
--eval_epochs Frequency of evaluation (in terms of epochs). 0.1
--save_epochs Frequency of saving the model (in terms of epochs). 0.25
--max_length Maximum sequence length(just to avoid OOM). 6144
--gradient_checkpointing Enable gradient checkpointing to save memory. False
--deepspeed Path to the DeepSpeed configuration file.(zero0, zero2, zero3) ds_config/zero0.json
--save_only_model Save only the model weights. True
--save_full_model Save the full model (including optimizer states, etc.). False
--dataloader_num_workers Number of workers for data loading. 8

By following these guidelines and understanding the parameters, you can customize the training process to suit your specific needs.