Datasets:

OpenVINO
/

documentation

@@ -14,7 +14,7 @@ That’s where tools like Intel [Hugging Face Optimum](https://docs.openvino.ai/
 Let’s first recap: A Vision Language Model (VLM) can understand both text and images. Instead of just reading or writing text, it can also “see” pictures, so you can ask it to describe a photo, answer a question about an image, or generate a caption. It’s like giving your LLM eyes.
-<figure class="image-gallery">
   <img src="https://huggingface.co/datasets/openvino/documentation/resolve/main/blog/openvino_vlm/chat1.png">
 </figure>
@@ -24,10 +24,11 @@ In contrast, SmolVLM is purpose-built for low-resource environments, and it beco
 Launched by Hugging Face in July 2024, SmolVLM addresses the growing need for multimodal AI that runs locally without requiring high-end GPUs or cloud infrastructure. As vision-language models become essential in areas like accessibility, robotics, and on-device assistants, SmolVLM offers a path to efficient, privacy-preserving inference at the edge.
 Architecturally, SmolVLM pairs a lightweight vision encoder with a compact language decoder. This modular design enables it to interpret both images and text.
-<figure class="image text-center">
-  <img src="https://huggingface.co/datasets/openvino/documentation/resolve/main/blog/openvino_vlm/smolvlm.png">
-  <figcaption> SmolVLM architecture (<b><i>Source: <a href="https://huggingface.co/blog/smolvlm#what-is-smolvlm">SmolVLM - small yet mighty Vision Language Model</i></b></a>).
- </figcaption>
 </figure>
 It offers a lightweight, efficient solution for running image-and-text models directly on laptops or edge devices.
@@ -73,7 +74,7 @@ Now it’s time to optimize the model for efficient execution using **quantizati
 Essentially, it's a way to map values from a high-precision data type, such as 32-bit floating-point numbers (FP32), to a lower-precision format, typically 8-bit integers (INT8). While this process offers several key benefits, it can also impact in a potential loss of accuracy.
-<figure class="image text-center">
   <img src="https://huggingface.co/datasets/openvino/documentation/resolve/main/blog/openvino_vlm/quantization.png">
 </figure>

 Let’s first recap: A Vision Language Model (VLM) can understand both text and images. Instead of just reading or writing text, it can also “see” pictures, so you can ask it to describe a photo, answer a question about an image, or generate a caption. It’s like giving your LLM eyes.
+<figure style="width: 700px; margin: 0 auto;">
   <img src="https://huggingface.co/datasets/openvino/documentation/resolve/main/blog/openvino_vlm/chat1.png">
 </figure>
 Launched by Hugging Face in July 2024, SmolVLM addresses the growing need for multimodal AI that runs locally without requiring high-end GPUs or cloud infrastructure. As vision-language models become essential in areas like accessibility, robotics, and on-device assistants, SmolVLM offers a path to efficient, privacy-preserving inference at the edge.
 Architecturally, SmolVLM pairs a lightweight vision encoder with a compact language decoder. This modular design enables it to interpret both images and text.
+<figure style="width: 700px; margin: 0 auto;">
+  <img src="https://huggingface.co/datasets/openvino/documentation/resolve/main/blog/openvino_vlm/smolvlm.png" width=700>
+  <figcaption style="text-align: center;">
+    SmolVLM architecture (<b><i>Source: <a href="https://huggingface.co/blog/smolvlm#what-is-smolvlm">SmolVLM - small yet mighty Vision Language Model</i></b></a>).
+  </figcaption>
 </figure>
 It offers a lightweight, efficient solution for running image-and-text models directly on laptops or edge devices.
 Essentially, it's a way to map values from a high-precision data type, such as 32-bit floating-point numbers (FP32), to a lower-precision format, typically 8-bit integers (INT8). While this process offers several key benefits, it can also impact in a potential loss of accuracy.
+<figure style="width: 800px; margin: 0 auto;">
   <img src="https://huggingface.co/datasets/openvino/documentation/resolve/main/blog/openvino_vlm/quantization.png">
 </figure>