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Celina

celinah

AI & ML interests

inference, on-device and image generation

Recent Activity

reacted to m-ric's post with πŸ”₯ 5 days ago
After 6 years, BERT, the workhorse of encoder models, finally gets a replacement: π—ͺ𝗲𝗹𝗰𝗼𝗺𝗲 π— π—Όπ—±π—²π—Ώπ—»π—•π—˜π—₯𝗧! πŸ€— We talk a lot about ✨Generative AI✨, meaning "Decoder version of the Transformers architecture", but this is only one of the ways to build LLMs: encoder models, that turn a sentence in a vector, are maybe even more widely used in industry than generative models. The workhorse for this category has been BERT since its release in 2018 (that's prehistory for LLMs). It's not a fancy 100B parameters supermodel (just a few hundred millions), but it's an excellent workhorse, kind of a Honda Civic for LLMs. Many applications use BERT-family models - the top models in this category cumulate millions of downloads on the Hub. ➑️ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT. π—§π—Ÿ;𝗗π—₯: πŸ›οΈ Architecture changes: β‡’ First, standard modernizations: - Rotary positional embeddings (RoPE) - Replace GeLU with GeGLU, - Use Flash Attention 2 ✨ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead. πŸ₯‡ As a result, the model tops the game of encoder models: It beats previous standard DeBERTaV3 for 1/5th the memory footprint, and runs 4x faster! Read the blog post πŸ‘‰ https://huggingface.co/blog/modernbert
upvoted a paper 5 days ago
Qwen2.5 Technical Report
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