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abdeljalil_elma

abdeljalilELmajjodi
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https://aelmajjodi.tiiny.site/

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upvoted an article about 7 hours ago
Welcome EmbeddingGemma, Google's new efficient embedding model
liked a dataset about 16 hours ago
data-agents/jupyter-agent-dataset
reacted to prithivMLmods's post with ❤️ about 18 hours ago
FastVLMs by Apple are the talk of the week for edge device VLMs and also for consumer-grade VLMs on the Hub. They have some impressive demos available on the Hub for live captioning and inference tasks. Meanwhile, I’m still exploring one of the coolest edge-device multimodal releases—Liquid AI’s LFM2-VL (450M and 1.6B). I’ve also made a live camera video inference demo, which is capable of running on Colab’s free-tier T4 GPU. 🤗Live Captioning Notebooks: ➠ LiquidAI LFM2 VL 1.6B Live Cam: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/LiquidAI-LFM2-VL-Live-Cam/LiquidAI_LFM2_VL_1_6B_Live_Cam.ipynb ➠ LiquidAI LFM2 VL 450M Live Cam: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/LiquidAI-LFM2-VL-Live-Cam/LiquidAI_LFM2_VL_450M_Live_Cam.ipynb ✨I also made a demo for the FastVLM Live Captioning Notebook. ➠ FastVLM 0.5B Live Cam: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/Apple-FastVLM-0.5B-Live-Cam/apple_FastVLM_0_5B_live_cam.ipynb ↗️For more notebooks, kindly visit the following repositories. ➠ Multimodal Outpost Notebooks: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks Feel free to fork, modify, and explore!
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