This is fantastic work, really impressive is an understatement. I really hope this sets a new DeepSeek-esque standard and starts another the death knell for companies continuing to cosplay as frontier labs (like Cohere).
Laguna S 2.1
41–50 of 98 posts
Re: Laguna S 2.1
#42!! Be careful when testing the model. A lot of people are testing it, and reporting disappointed results / benchmaxxxing claim. But do not realize that thinking has a issue with the default configuration. Important - make sure that THINKING is enabled. By default it wasn't although I was passing the flag --default-chat-template-kwargs '{"enable_thinking": true}' in vllm recipe. The generation_config.json file that is…
Thanks for posting this, it made a huge difference tweaking the recipe.
Re: Laguna S 2.1
#43Has anyone tried it on a mac yet?
Re: Laguna S 2.1
#44Earlier quoted context omitted.
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.
> edit: on bigger tests, got it to loop pretty easily unfortunately, probably local settings.
Been playing around for a few hours with the poolside/Laguna-S-2.1-NVFP4 + poolside/Laguna-S-2.1-DFlash-NVFP4 + vLLM, been seeing the same behaviour. Usually new model releases are plagued with issues at release though, best to wait 1-2 weeks then retry, or better yet, investigate yourself :) Personally I haven't found any obvious issues.
Re: Laguna S 2.1
#45You will not, directly or indirectly:
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Re: Laguna S 2.1
#46[dead]
Re: Laguna S 2.1
#47Looks 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…
> if someone would kindly quantise this down for the 64GB paupers
The Q4_K_M is 75GB. I'm exactly at 64GB, and I wouldn't quantize it further. Instead, do partial weight residency and stream the rest from SSD.
Re: Laguna S 2.1
#48initial impressions, great model for coding, probably swapping it out for qwen 27b for a while to long-term test, more sycophantic than any I've run locally myself
Re: Laguna S 2.1
#49Testing 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…
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Re: Laguna S 2.1
#50Immediate reaction is that it seems to be a bit behind Meta Muse Spark 1.1 performance at approximately the Deepseek v4 Flash price point. That's quite good given Muse Spark benchmarks a lot better than Deepseek v4 Flash (assuming benchmarks mean anything, which they don't).