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Laguna S 2.1
21–30 of 98 posts
Re: Laguna S 2.1
#22Hey, this model is not a joke! Exciting, we already got a usable PR of work out of it.
Re: Laguna S 2.1
#23Looks impressive, and this size fits achievable home hardware.
That said, if someone would kindly quantise this down for the 64GB paupers, that would be appreciated. (I know there’s likely degradation, but some people reported good results with a 2 bit version of Qwen 3.5 122B, and this is starting from a higher point. Would be interesting to try, at least.)
Edit: someone in the process of doing so: https://huggingface.co/vcruz305/Laguna-S-2.1-GGUF
Re: Laguna S 2.1
#24Testing it now. At the very least, competitive with DS4-Flash indeed. On my small (and per Sol's words, _very_ semantically dense) C test codebase, it found things that only gpt-5.2 managed to find back in the day, but also made a stupidly incorrect initial observation that a memfd_create()/mmap was used for IPC (funnily enough - sol missed that as well in its review, until I pointed it out). Re: the claims vs deepse…
What harness/quant did you use for testing?
nvfp4 mlx, literally barebones pi.
edit: on bigger tests, got it to loop pretty easily unfortunately, probably local settings.
Re: Laguna S 2.1
#25Whoa 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.
If the numbers are legitimate then our prayers have been heard
Re: Laguna S 2.1
#26Looks impressive, and this size fits achievable home hardware. That said, if someone would kindly quantise this down for the 64GB paupers, that would be appreciated. (I know there’s likely degradation, but some people reported good results with a 2 bit version of Qwen 3.5 122B, and this is starting from a higher point. Would be interesting to try, at least.) Edit: someone in the process of doing so: https://huggingfa…
The tool I've been using, llm-compressor, can quant models that do not fit in memory (use the sequential pipeline)
https://github.com/vllm-project/llm-compressor
my setup to help you on your way: https://github.com/verdverm/quantr
Though it seems these will not be needed as Poolside has published quants & dflash with their models.
Re: Laguna S 2.1
#27Earlier quoted context omitted.
Having played with it for like 3 hours now....I'm probably moving from CC to this
I am about 1 hour into using it with pi.dev. Do you have thinking on high? It is doing good but at one point i had to stop it and say 'you're overthinking this' haha
Yes full send mode on thinking. I have moved on from watching my agents and I don't really care how it thinks. I look at the end result and so far this thing has been blowing me away. No way this is as good as it is this small and fast. Outside Fable, this might be the best thing I've ever used.
Re: Laguna S 2.1
#28Model 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!
I hope all models going forward come with a dflash drafter so we don't have to train one up separately.
Re: Laguna S 2.1
#29Looks impressive, and this size fits achievable home hardware. That said, if someone would kindly quantise this down for the 64GB paupers, that would be appreciated. (I know there’s likely degradation, but some people reported good results with a 2 bit version of Qwen 3.5 122B, and this is starting from a higher point. Would be interesting to try, at least.) Edit: someone in the process of doing so: https://huggingfa…
They have also published smaller 33B model called Laguna XS 2.1, its Q4 gguf is 20GB.
https://huggingface.co/poolside/Laguna-XS-2.1-GGUF/tree/main
Re: Laguna S 2.1
#30Congrats Poolside team!!