Working on the representations of LMs and pretraining methods
https://nathangodey.github.io
We trained 3 models - 1.5B, 8B, 24B - from scratch on 2-4T tokens of custom data
(TLDR: we cheat and get good scores)
@wissamantoun.bsky.social @rachelbawden.bsky.social @bensagot.bsky.social @zehavoc.bsky.social
We trained 3 models - 1.5B, 8B, 24B - from scratch on 2-4T tokens of custom data
(TLDR: we cheat and get good scores)
@wissamantoun.bsky.social @rachelbawden.bsky.social @bensagot.bsky.social @zehavoc.bsky.social
We trained 3 models - 1.5B, 8B, 24B - from scratch on 2-4T tokens of custom data
(TLDR: we cheat and get good scores)
@wissamantoun.bsky.social @rachelbawden.bsky.social @bensagot.bsky.social @zehavoc.bsky.social
You can read his PhD online here: hal.science/tel-04994414/
You can read his PhD online here: hal.science/tel-04994414/
We introduce Q-Filters, a training-free method for efficient KV Cache compression!
It is compatible with FlashAttention and can compress along generation which is particularly useful for reasoning models ⚡
TLDR: we make Streaming-LLM smarter using the geometry of attention
We introduce Q-Filters, a training-free method for efficient KV Cache compression!
It is compatible with FlashAttention and can compress along generation which is particularly useful for reasoning models ⚡
TLDR: we make Streaming-LLM smarter using the geometry of attention