model:
  arch: minigpt4
  model_type: pretrain_llama2
  max_txt_len: 160
  end_sym: "</s>"
  low_resource: True
  prompt_template: '[INST] {} [/INST] '
  ckpt: 'please set this value to the path of pretrained checkpoint'
  
datasets:
  coco_vqa: # not used
    type: eval
    vis_processor:
      eval:
        name: "blip2_image_eval"
        image_size: 336
    text_processor:
      eval:
        name: "blip_caption"

run:
  task: vqa
  # optimization-specific
  batch_size_train: 32
  batch_size_eval: 128
  # if do not use OCR token: batch=32, Evaluation Total time: 0:23:47 (9.0954 s / it)
  use_ocr: True
  # use OCR token: batch_size=16; Evaluation Total time: 0:36:34 (7.0116 s / it)
  num_workers: 8

  # inference-specific
  max_len: 10
  min_len: 1
  num_beams: 1
  inference_method: "generate"
  prompt: "Question: {} Short answer:"

  seed: 42
  output_dir: "results"

  evaluate: True
  test_splits: ["val"]

  # distribution-specific
  device: "cuda"
  world_size: 1
  dist_url: "env://"
  distributed: True