Reaching 1B Context Length With RAG

Zyphra: https://www.zyphra.com/post/reaching-1b-context-length-with-rag

retrieval system enables LLMs to process up to 1 billion tokens efficiently on a standard CPU using a sparse graph-based approach. Outperforms RAG methods with dense embeddings or long-context transformers.

I’m impressed with the work Zyphra has been doing in the SSM space (most recently Zamba2-7B) so I’m eager to see more.

chungus raw prompt go in yum yum machine, make yummy portion prompt for llm come out.

Zyphra's ultra fast more general RAG search is yum yum machine

aichannel

product @seedclub. i host S-TIER @ X Spaces & i interview crypto builders @ web3galaxybrain.com.

ofc post where you please. but do check out /aichannel where less scientific takes are allowed 

i'll post them in /six for now on we like to keep /ai a little more scientific