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dillfrescott

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https://dill.moe

dillfrescott

AI & ML interests

AI, anime, computers

Recent Activity

new activity 4 minutes ago

fishaudio/fish-speech-1.5:That is not free software, as you forbid commercial use

reacted to m-ric's post with 👍 about 11 hours ago

After 6 years, BERT, the workhorse of encoder models, finally gets a replacement: 𝗪𝗲𝗹𝗰𝗼𝗺𝗲 𝗠𝗼𝗱𝗲𝗿𝗻𝗕𝗘𝗥𝗧! 🤗 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. ➡️ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT. 𝗧𝗟;𝗗𝗥: 🏛️ Architecture changes: ⇒ 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. 🥇 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 👉 https://huggingface.co/blog/modernbert

reacted to m-ric's post with 🔥 about 11 hours ago

After 6 years, BERT, the workhorse of encoder models, finally gets a replacement: 𝗪𝗲𝗹𝗰𝗼𝗺𝗲 𝗠𝗼𝗱𝗲𝗿𝗻𝗕𝗘𝗥𝗧! 🤗 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. ➡️ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT. 𝗧𝗟;𝗗𝗥: 🏛️ Architecture changes: ⇒ 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. 🥇 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 👉 https://huggingface.co/blog/modernbert

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