## Diagram: jina-vlm Model Architecture
### Overview
The image presents a diagram illustrating the architecture of the jina-vlm model. It shows the flow of data from an input image and prompt through several processing stages, ultimately generating a text output. The diagram includes components like image tiling, SIGLIP2, VL-Connector, input tokens, and QWEN3 Decoder.
### Components/Axes
* **Header:** Contains the input image, prompt, and output.
* **Input Image:** A photograph of a woman with a hat. Dimensions are 2728 x 2046.
* **Prompt:** "what is the name of this lady?" Represented by 6 teal-colored blocks.
* **Output:** "The name is Lenna (or Lena)." Represented by 9 yellow-colored blocks.
* **Main Diagram:** Illustrates the model architecture.
* **jina-vlm:** Title of the model.
* **Tiling:** Shows the input image being divided into 12 tiles plus a thumbnail. Dimensions are 1176 x 918.
* Each tile is processed, resulting in 378-378 features, multiplied by 13.
* **SIGLIP2:** A block with approximately 400M parameters.
* Contains Transformer Blocks 27 to 1.
* Layer 18 and Layer 24 are indicated.
* Each layer produces 27-27-2384 features, multiplied by 13.
* **VL-Connector:** A block with approximately 50M parameters.
* Contains 2x2 Attention Pooling and MLP Projection.
* Outputs 182 tokens, multiplied by 13.
* **Input Tokens:** Represented by a grid of red and teal blocks.
* 2366 image tokens + text tokens.
* **QWEN3 Decoder:** A block with approximately 1.7B parameters.
* Contains Transformer Blocks 28 to 1.
### Detailed Analysis
* **Image Tiling:** The input image is divided into a grid of 12 tiles, with an additional thumbnail. Each tile is processed to extract features.
* **SIGLIP2:** This component processes the tiled image features using a series of Transformer blocks. The number of parameters is approximately 400 million.
* **VL-Connector:** This component connects the visual features from SIGLIP2 with the text prompt. It uses attention pooling and MLP projection. The number of parameters is approximately 50 million.
* **Input Tokens:** The output of the VL-Connector is converted into a sequence of tokens. The diagram indicates 2366 image tokens and some text tokens. The tokens are represented by red blocks, while the text tokens are represented by teal blocks.
* **QWEN3 Decoder:** This component decodes the input tokens to generate the final text output. It uses a series of Transformer blocks. The number of parameters is approximately 1.7 billion.
### Key Observations
* The diagram illustrates a multi-stage process for image captioning or visual question answering.
* The model uses a combination of visual and textual information to generate the output.
* The diagram highlights the key components of the model and their interconnections.
### Interpretation
The diagram provides a high-level overview of the jina-vlm model architecture. It demonstrates how the model processes an input image and prompt to generate a text output. The model uses a combination of image tiling, Transformer blocks, attention pooling, and MLP projection to extract visual features, connect them with the text prompt, and generate the final output. The diagram suggests that the model is designed to perform tasks such as image captioning or visual question answering. The use of a large number of parameters in each component indicates that the model is capable of learning complex relationships between visual and textual information.
