🕳️ Attention Sinks in LLMs for endless fluency

Tom Aarsen huggingface.co

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Thumbnail of Ahead of AI #12: LLM Businesses and Busyness

Sebastian Raschka • Ahead of AI #12: LLM Businesses and Busyness

Text embeddings are a critical piece of many pipelines, from search, to RAG, to vector databases and more. Most embedding models are BERT/Transformer-based and typically have short context lengths (e.g., 512). That’s only about two pages of text, but documents can be very long – books, legal cases, TV screenplays, code repositories, etc can be tens... See more

GitHub - mit-han-lab/streaming-llm: Efficient Streaming Language Models with Attention Sinks

What We Learned From a Year of Building With LLMs

All the Hard Stuff Nobody Talks About When Building Products With LLMs

From Notetaking to Neuralink

Sebastian Raschka • Ahead of AI #12: LLM Businesses and Busyness

Long-Context Retrieval Models with Monarch Mixer