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Mex Ivanov

MexIvanov
21world's profile picture evilfreelancer's profile picture
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AI & ML interests

NLP, Coding, Quantum Computing and more.

Recent Activity

reacted to m-ric's post with ๐Ÿš€ 7 days ago
Since I published it on GitHub a few days ago, Hugging Face's new agentic library ๐˜€๐—บ๐—ผ๐—น๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐˜€ has gathered nearly 4k stars ๐Ÿคฏ โžก๏ธ But we are just getting started on agents: so we are hiring an ML Engineer to join me and double down on this effort! The plan is to build GUI agents: agents that can act on your computer with mouse & keyboard, like Claude Computer Use. We will make it work better, and fully open. โœจ Sounds like something you'd like to do? Apply here ๐Ÿ‘‰ https://apply.workable.com/huggingface/j/AF1D4E3FEB/
reacted to m-ric's post with ๐Ÿ”ฅ 7 days ago
Since I published it on GitHub a few days ago, Hugging Face's new agentic library ๐˜€๐—บ๐—ผ๐—น๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐˜€ has gathered nearly 4k stars ๐Ÿคฏ โžก๏ธ But we are just getting started on agents: so we are hiring an ML Engineer to join me and double down on this effort! The plan is to build GUI agents: agents that can act on your computer with mouse & keyboard, like Claude Computer Use. We will make it work better, and fully open. โœจ Sounds like something you'd like to do? Apply here ๐Ÿ‘‰ https://apply.workable.com/huggingface/j/AF1D4E3FEB/
reacted to singhsidhukuldeep's post with ๐Ÿ”ฅ 22 days ago
Exciting News in AI: JinaAI Releases JINA-CLIP-v2! The team at Jina AI has just released a groundbreaking multilingual multimodal embedding model that's pushing the boundaries of text-image understanding. Here's why this is a big deal: ๐Ÿš€ Technical Highlights: - Dual encoder architecture combining a 561M parameter Jina XLM-RoBERTa text encoder and a 304M parameter EVA02-L14 vision encoder - Supports 89 languages with 8,192 token context length - Processes images up to 512ร—512 pixels with 14ร—14 patch size - Implements FlashAttention2 for text and xFormers for vision processing - Uses Matryoshka Representation Learning for efficient vector storage โšก๏ธ Under The Hood: - Multi-stage training process with progressive resolution scaling (224โ†’384โ†’512) - Contrastive learning using InfoNCE loss in both directions - Trained on massive multilingual dataset including 400M English and 400M multilingual image-caption pairs - Incorporates specialized datasets for document understanding, scientific graphs, and infographics - Uses hard negative mining with 7 negatives per positive sample ๐Ÿ“Š Performance: - Outperforms previous models on visual document retrieval (52.65% nDCG@5) - Achieves 89.73% image-to-text and 79.09% text-to-image retrieval on CLIP benchmark - Strong multilingual performance across 30 languages - Maintains performance even with 75% dimension reduction (256D vs 1024D) ๐ŸŽฏ Key Innovation: The model solves the long-standing challenge of unifying text-only and multi-modal retrieval systems while adding robust multilingual support. Perfect for building cross-lingual visual search systems! Kudos to the research team at Jina AI for this impressive advancement in multimodal AI!
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