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add openvino VLM blog post #3071
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Co-authored-by: Helena Kloosterman <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
* Blog updates * updates
Thanks a lot for the great review @pcuenca, didn't had time to include everything but will do in a second pass. The blog post is not ready for publication yet but once it is, I'll let you know |
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super cool!
Thanks a lot for your reviews @pcuenca @merveenoyan! The blog post is not ready yet (was set to draft but I should have clarified it in the description). Likely a lot will change in the following days, so don't want you to waste your time on corrections that could very well not be included in the final blog post, will let you know once ready ! |
Co-authored-by: Nikita Savelyev <[email protected]>
openvino-vlm.md
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This benchmark shows that small, optimized multimodal models, like [SmolVLM2-256M](https://huggingface.co/HuggingFaceTB/SmolVLM2-256M-Video-Instruct), can run efficiently on Intel CPUs. Weight-only quantization significantly reduces model size, improving efficiency without majorly impacting throughput. |
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cc @ezelanza would you mind updating once the benchmark is validated on your side
Co-authored-by: Nikita Savelyev <[email protected]>
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Co-authored-by: Eze Lanza (Eze) <[email protected]>
Co-authored-by: Eze Lanza (Eze) <[email protected]>
Co-authored-by: Eze Lanza (Eze) <[email protected]>
from https://huggingface.co/datasets/OpenVINO/documentation/blob/main/blog/openvino_vlm/openvino-vlm.md