text: clip_vit_b32 = {
      "description": "OpenAI's recently released CLIP model — when supplied with a list of labels and an image, CLIP can accurately predict which labels best fit the provided image.",
      "tasks": ["image-classification", "image-vectorisation", "text-vectorisation", "image-text-vectorisation"],
      "init_kwargs": {
          "model_path": "ViT-B/32"
      },
      "details": {
          "num_parameters": 151277313,
          "max_text_length": 77,
          "image-classification": {
              "zero_shot": True
          },
          "image-vectorisation": {
              "finetunable": True,
              "vector_size": 512
          },
          "text-vectorisation": {
              "finetunable": True,
              "vector_size": 512,
          },
          "image-text-vectorisation": {
              "finetunable": True,
              "vector_size": 1024,
          },
          "credits": [
              {
                  "name": "OpenAI",
                  "url": "https://openai.com/blog/clip/"
              }
          ]
      }
  }
  
  # TODO: RN50 etc
  models = {
      "clip-vit-b32": clip_vit_b32,
  }