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Laguna S 2.1 (poolside.ai)
kamranjon 2 minutes ago [-]
Whoa whoa whoa, 118b params, 8b active MOE, long context reasoning, open weights - music to my ears. Hadn't heard of this lab before but I am very excited, will definitely try this out tomorrow - this is a real sweet spot I think in terms of model size and performance.
SwellJoe 9 minutes ago [-]
This is exactly the kind of model that's been needed in the middle. Realistically self-hosted, Good Enough intelligence, MoE so it's fast on limited bandwidth systems like Strix Halo and DGX Spark.

For a while there's been nothing to run on my Strix Halo that's notably better than what I can run on my dual 32GB GPU desktop (Gemma 4 or Qwen 3.6 dense models), but this seems likely to be the step up in size that actually works better than those.

river_otter 6 minutes ago [-]
I love this. Is it possible to give a feel of how this stacks up to the good old Opus 4.5 in coding quality? For me that was the turning point where agentic coding in Claude Code etc became usable. Have we hit that threshold?
Iolaum 32 minutes ago [-]
Model Looks amazing!

Even more important, subjectively, is that this model will run very well on Strix Halo (e.g. Framework Desktop), DGX Spark kinds of devices. Looking forward to Unsloth dynamic mtp quants.

P.S. Looking at the HF release they already offer Q4_K_M and DFlash drafter for speculative decoding!

megavon 1 hours ago [-]
This is INSANE. How did they do this?
eisokant 33 minutes ago [-]
"What we've done in this model is not necessarily add more intelligence, but improve the behaviors that lead to a more capable model: more verification, less taking things for granted, not declaring victory early, and being more persistent.”

+

https://poolside.ai/assets/laguna/laguna-m1-xs2-technical-re...

34 minutes ago [-]
36 minutes ago [-]
tosh 1 hours ago [-]
open weights and

similar performance to deepseek v4, inkling at size of nemotron 3 super (!)

iraldir 1 hours ago [-]
Amazing model at this size if true, that's quite crazy!
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