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Laguna S 2.1

poolside.ai

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Re: Laguna S 2.1

#81

Earlier quoted context omitted.

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.

Nice! Do you know of any tools that do this for tensorrt models?

Sorry, I don't, still newer to the quantizing side of things since many are already produced by others.

Re: Laguna S 2.1

#82
post #73

Earlier quoted context omitted.

What's your hardware? I've got 64GB RAM and a 4090 here, wondering if it's worth a play.

It’s the Strix halo (AMD) with 128 GB shared memory. The 4bit quant is ~75GB. Unfortunately I don’t know about the best way of running on an Nvidia gpu, you could try llama.cpp and offloading as many layers as possible into the gpu and using RAM for the rest, not sure if that would slow it down too much though.

I got usable token rates (10-20tps from memory, so marginal) with the Qwen A10B a while back, well before all the new speculative speedups landed in llama.cpp. There's an unmerged branch which allegedly supports this, but I don't know how well yet. Looks worth investigating but I might give it a few days to see what bugs get shaken loose.

Re: Laguna S 2.1

#83

Earlier quoted context omitted.

Update3, summarized from RTX6kPRO Discord: > The BF16 checkpoint doesn't exhibit any of the complaints [...] with our current quants, the models tend to choose the wrong logits sometimes [...] why we'll need a requant [...] We're not aware of any bugs in any runtimes themselves [...] We have two remaining things that we're trying to tackle: some people are reporting thinking being too hard to trigger (^^) , and other…

Deleted earlier, didn't see you post, pasting here: /* Just started testing with the gguf (with gpu offload, m5 max 128gb), q4_k_m, running seemingly well. Speed is initially slightly faster than antirez/ds4 - decode tok/s in the 30s, prefill ~400-ish. Expected, given the slightly smaller size. Looks to be working fine, but too early to tell. Definitely likes to "think". */ Anyways, guessing that discord might be foc…

Well, just ran said gguf on the GeneralsX codebase with a pretty open-ended "Explain this codebase to me, and the general game loop." prompt, and...

  Let me also look at the GameLogic::update() to see the rest of the update flow, especially the object update loop.

  Actually, I think I have enough information now. Let me also check the GameLogic::UPDATE to understand the full update flow.
...repeating forever.

Since they mentioned that they're working on new quants, guess I'll wait. From earlier tests on work stuff, it's definitely capable.

Re: Laguna S 2.1

#85

Earlier quoted context omitted.

Deleted earlier, didn't see you post, pasting here: /* Just started testing with the gguf (with gpu offload, m5 max 128gb), q4_k_m, running seemingly well. Speed is initially slightly faster than antirez/ds4 - decode tok/s in the 30s, prefill ~400-ish. Expected, given the slightly smaller size. Looks to be working fine, but too early to tell. Definitely likes to "think". */ Anyways, guessing that discord might be foc…

Well, just ran said gguf on the GeneralsX codebase with a pretty open-ended "Explain this codebase to me, and the general game loop." prompt, and... Let me also look at the GameLogic::update() to see the rest of the update flow, especially the object update loop. Actually, I think I have enough information now. Let me also check the GameLogic::UPDATE to understand the full update flow. ...repeating forever. Since the…

Right:

https://huggingface.co/poolside/Laguna-S-2.1-FP8/discussions...

Wait time.

Re: Laguna S 2.1

#86

Earlier quoted context omitted.

Well, just ran said gguf on the GeneralsX codebase with a pretty open-ended "Explain this codebase to me, and the general game loop." prompt, and... Let me also look at the GameLogic::update() to see the rest of the update flow, especially the object update loop. Actually, I think I have enough information now. Let me also check the GameLogic::UPDATE to understand the full update flow. ...repeating forever. Since the…

Right: https://huggingface.co/poolside/Laguna-S-2.1-FP8/discussions... Wait time.

More updates from the Discord (also mostly about the NVFP4):

> we've got an NVFP4 checkpoint that has a KD of 0.135 relative to the BF16 checkpoint (for W4A4, on some GSM8K prompts). [...] We're still seeing some looping on W4A4 (although much less than before), whereas for W4A16 we've not been able to make it loop. Given that W4A4 is still a little broken, we won't make this an official release (we have one more idea planned to fix that). [...] The RC1 for NVFP4 is reachable as poolside/Laguna-S-2.1-NVFP4 @ RC1

Seems to be available already indeed: https://huggingface.co/poolside/Laguna-S-2.1-NVFP4/tree/RC1

Re: Laguna S 2.1

#87
post #9

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 actual…

On Strix Halo, have a look at the work antirez is doing with dwarfstar (antirez/ds4) for deepseek v4 flash at reasonable speeds and quality.

Re: Laguna S 2.1

#88
post #9

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 actual…

On Strix Halo, have a look at the work antirez is doing with dwarfstar (antirez/ds4) for deepseek v4 flash at reasonable speeds and quality.

I've tried it, but, it's not reasonable speed, at all. It's 9-13 tokens per second, which is not usable interactively and not worth using for long-running API stuff when DeepSeek V4 Pro is so cheap via their API.

Laguna S 2.1 runs at 15-28 tokens per second, depending on context and...something about how long it's been running, which is very comfortable for chatting, but still not usable for interactive agentic coding. Their `pool` agent just times out when I try to use it with the Strix Halo-hosted instance of the model.

Re: Laguna S 2.1

#89

Earlier quoted context omitted.

Right: https://huggingface.co/poolside/Laguna-S-2.1-FP8/discussions... Wait time.

More updates from the Discord (also mostly about the NVFP4): > we've got an NVFP4 checkpoint that has a KD of 0.135 relative to the BF16 checkpoint (for W4A4, on some GSM8K prompts). [...] We're still seeing some looping on W4A4 (although much less than before), whereas for W4A16 we've not been able to make it loop. Given that W4A4 is still a little broken, we won't make this an official release (we have one more ide…

Tool calling in pi completely broke with the updated q4 gguf (spinquant-less). Guessing it'll take some time.

Re: Laguna S 2.1

#90
post #8

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!

I hope all models going forward come with a dflash drafter so we don't have to train one up separately.

Also, that all models going forward target the base prosumer class ie today = Strix Halo
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