Simone Scardapane
@sscardapane.bsky.social
I fall in love with a new #machinelearning topic every month 🙄
Ass. Prof. Sapienza (Rome) | Author: Alice in a differentiable wonderland (https://www.sscardapane.it/alice-book/)
Ass. Prof. Sapienza (Rome) | Author: Alice in a differentiable wonderland (https://www.sscardapane.it/alice-book/)
Reposted by Simone Scardapane
Thanks a lot to all my amazing co-authors @alessiodevoto.bsky.social @sscardapane.bsky.social @yuzhaouoe.bsky.social @neuralnoise.com Eric de la Clergerie @bensagot.bsky.social
And a special thanks to @edoardo-ponti.bsky.social for the academic visit that made this work possible!
And a special thanks to @edoardo-ponti.bsky.social for the academic visit that made this work possible!
March 6, 2025 at 4:02 PM
Thanks a lot to all my amazing co-authors @alessiodevoto.bsky.social @sscardapane.bsky.social @yuzhaouoe.bsky.social @neuralnoise.com Eric de la Clergerie @bensagot.bsky.social
And a special thanks to @edoardo-ponti.bsky.social for the academic visit that made this work possible!
And a special thanks to @edoardo-ponti.bsky.social for the academic visit that made this work possible!
Reposted by Simone Scardapane
Q-Filters is very efficient which allows streaming compression at virtually no latency cost, just like Streaming-LLM...
...but it is also much better at retaining relevant KV pairs compared to fast alternatives (and can even beat slower algorithms such as SnapKV)
...but it is also much better at retaining relevant KV pairs compared to fast alternatives (and can even beat slower algorithms such as SnapKV)
March 6, 2025 at 1:41 PM
Q-Filters is very efficient which allows streaming compression at virtually no latency cost, just like Streaming-LLM...
...but it is also much better at retaining relevant KV pairs compared to fast alternatives (and can even beat slower algorithms such as SnapKV)
...but it is also much better at retaining relevant KV pairs compared to fast alternatives (and can even beat slower algorithms such as SnapKV)