Datasets:

OpenVINO
/

documentation

@@ -18,14 +18,6 @@ Let’s first recap: A Vision Language Model (VLM) can understand both text and
   <img src="https://huggingface.co/datasets/openvino/documentation/resolve/main/blog/openvino_vlm/chat1.png">
 </figure>
-<figure class="image text-center">
-  <img src="https://huggingface.co/datasets/openvino/documentation/resolve/main/blog/openvino_vlm/chat2.png">
-</figure>
-<figure class="image text-center">
-  <img src="https://huggingface.co/datasets/openvino/documentation/resolve/main/blog/openvino_vlm/chat3.png">
-</figure>
 It’s impressive, but not exactly accessible to use. Let’s take [CogVLM](https://github.com/THUDM/CogVLM), for example, it is a powerful open source vision-language model with around 17 billion parameters (10B vision encoder \+ 7B language model)  which can require [about 80GB of RAM](https://inference.roboflow.com/foundation/cogvlm/) to run the model in full precision. Inference is still relatively slow: captioning a single image takes 10 to 13 seconds on an NVIDIA T4 GPU ([RoboflowBenchmark](https://inference.roboflow.com/foundation/cogvlm/?utm_source=chatgpt.com)). Users attempting to run CogVLM on CPUs have reported crashes or memory errors even with 64 GB of RAM, highlighting its impracticality for typical local deployment ([GitHub Issue](https://github.com/THUDM/CogVLM/issues/162)), just to mention one model, this is the challenge faced recently with most small VLMs.
 In contrast, SmolVLM is purpose-built for low-resource environments, and it becomes a highly efficient solution for deploying vision-language models on laptops or edge devices.
@@ -137,6 +129,7 @@ print(generated_texts[0])
 ```
 Try the complete notebook [here](https://github.com/huggingface/optimum-intel/blob/main/notebooks/openvino/vision_language_quantization.ipynb).
 ## Conclusion
 Multimodal AI is becoming more accessible thanks to smaller, optimized models like **SmolVLM**, along with tools such as **Hugging Face Optimum** and **OpenVINO**. While deploying vision-language models locally still comes with challenges, this workflow shows that it's possible to run lightweight image-and-text models on modest hardware.

   <img src="https://huggingface.co/datasets/openvino/documentation/resolve/main/blog/openvino_vlm/chat1.png">
 </figure>
 It’s impressive, but not exactly accessible to use. Let’s take [CogVLM](https://github.com/THUDM/CogVLM), for example, it is a powerful open source vision-language model with around 17 billion parameters (10B vision encoder \+ 7B language model)  which can require [about 80GB of RAM](https://inference.roboflow.com/foundation/cogvlm/) to run the model in full precision. Inference is still relatively slow: captioning a single image takes 10 to 13 seconds on an NVIDIA T4 GPU ([RoboflowBenchmark](https://inference.roboflow.com/foundation/cogvlm/?utm_source=chatgpt.com)). Users attempting to run CogVLM on CPUs have reported crashes or memory errors even with 64 GB of RAM, highlighting its impracticality for typical local deployment ([GitHub Issue](https://github.com/THUDM/CogVLM/issues/162)), just to mention one model, this is the challenge faced recently with most small VLMs.
 In contrast, SmolVLM is purpose-built for low-resource environments, and it becomes a highly efficient solution for deploying vision-language models on laptops or edge devices.
 ```
 Try the complete notebook [here](https://github.com/huggingface/optimum-intel/blob/main/notebooks/openvino/vision_language_quantization.ipynb).
 ## Conclusion
 Multimodal AI is becoming more accessible thanks to smaller, optimized models like **SmolVLM**, along with tools such as **Hugging Face Optimum** and **OpenVINO**. While deploying vision-language models locally still comes with challenges, this workflow shows that it's possible to run lightweight image-and-text models on modest hardware.