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Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model

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Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model

#141

Post by logicprog »

PlatoIsADisease wrote: post #132
bertili wrote: post #34

The "Deepseek moment" is just one year ago today! Coincidence or not, let's just marvel for a second over this amount of magic/technology that's being given away for free... and how liberating and different this is than OpenAI and others that were closed to "protect us all".

I am convinced that was mostly just marketing. No one uses deepseek as far as I can tell. People are not running it locally. People choose GPT/Gemini/Claude/Grok if you are giving your data away anyway. My biggest source of my conspiracy is that I made a reddit thread asking a question: "Why all the deepseek hype" or something like that. And to this day, I get odd, 'pro deepseek' comments from accounts only used ever…

I don't use DeepSeek, but I prefer Kimi and GLM to closed models for most of my work.

Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model

#142

Post by selfhoster11 »

WhitneyLand wrote: post #129
johndough wrote: post #81

Earlier quoted context omitted.

The model absolutely can be run at home. There even is a big community around running large models locally: https://www.reddit.com/r/LocalLLaMA/ The cheapest way is to stream it from a fast SSD, but it will be quite slow (one token every few seconds). The next step up is an old server with lots of RAM and many memory channels with maybe a GPU thrown in for faster prompt processing (low two digits tokens/second). At t…

Its often pointed out in the first sentence of a comment how a model can be run at home, then (maybe) towards the end of the comment it’s mentioned how it’s quantized. Back when 4k movies needed expensive hardware, no one was saying they could play 4k on a home system, then later mentioning they actually scaled down the resolution to make it possible. The degree of quality loss is not often characterized. Which makes…

Except the parent comment said you can stream the weights from an SSD. The full weights, uncompressed. It takes a little longer (a lot longer), but the model at least works without lossy pre-processing.

Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model

#143

Post by esafak »

PlatoIsADisease wrote: post #130
johndough wrote: post #81

Earlier quoted context omitted.

The model absolutely can be run at home. There even is a big community around running large models locally: https://www.reddit.com/r/LocalLLaMA/ The cheapest way is to stream it from a fast SSD, but it will be quite slow (one token every few seconds). The next step up is an old server with lots of RAM and many memory channels with maybe a GPU thrown in for faster prompt processing (low two digits tokens/second). At t…

>The model absolutely can be run at home. There is a huge difference between "look I got it to answer the prompt: '1+1='" and actually using it for anything of value. I remember early on people bought Macs (or some marketing team was shoveling it), and proposing people could reasonably run the 70B+ models on it. They were talking about 'look it gave an answer', not 'look this is useful'. While it was a bit obvious th…

Hear, hear. Even if the model fits, a few tokens per second make no sense. Time is money too.

Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model

#144

Post by esafak »

Alifatisk wrote: post #53

Have you all noted that the latest releases (Qwen3 max thinking, now Kimi k2.5) from Chinese companies are benching against Claude opus now and not Sonnet? They are truly catching up, almost at the same pace?

They are, in benchmarks. In practice Anthropic's models are ahead of where their benchmarks suggest.

Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model

#145

Post by IanCal »

XCSme wrote: post #104
mzl wrote: post #98

Earlier quoted context omitted.

An LLM model only outputs tokens, so this could be seen as an extension of tool calling where it has trained on the knowledge and use-cases for "tool-calling" itself as a sub-agent.

Ok, so agent swarm = tool calling where the tool is a LLM call and the argument is the prompt

Yes largely, although they’ve trained a model specifically for this task rather than using the base model and a bit of prompting.

Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model

#146

Post by 9cb14c1ec0 »

dev_l1x_be wrote: post #115
whizzter wrote: post #92

Earlier quoted context omitted.

Chinese state that maybe sees open collaboration as the way to nullify any US lead in the field, concurrently if the next "search-winner" is built upon their model the Chinese worldview that Taiwan belongs to China and Tiamen Square massacre never happened. Also their license says that if you have a big product you need to promote them, remember how Google "gave away" site searche widgets and that was perhaps one of…

> Taiwan belongs to China So they are on the same page as the UN and US? The One China policy refers to a United States policy of strategic ambiguity regarding Taiwan.[1] In a 1972 joint communiqué with the PRC, the United States "acknowledges that all Chinese on either side of the Taiwan Strait maintain there is but one China and that Taiwan is a part of China" and "does not challenge that position." https://en.wiki…

The One China policy is a fiction of foreign policy statecraft, designed to sideline the issue without having to actually deal with it. It is quite clear that apart from the official fiction there is a real policy that is not One China. This is made clear by the weapons sales to Taiwan that specifically calibrated to make a Chinese military action harder.

Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model

#147

Post by [deleted] »

Balinares wrote: post #100
throwaw12 wrote: post #67

Congratulations, great work Kimi team. Why is that Claude still at the top in coding, are they heavily focused on training for coding or is it their general training is so good that it performs well in coding? Someone please beat the Opus 4.5 in coding, I want to replace it.

I replaced Opus with Gemini Pro and it's just plain a better coder IMO. It'll restructure code to enable support for new requirements where Opus seems to just pile on more indirection layers by default, when it doesn't outright hardcode special cases inside existing functions, or drop the cases it's failing to support from the requirements while smugly informing you you don't need that anyway.

[deleted]

Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model

#148

Post by 1dom »

zamadatix wrote: post #137
1dom wrote: post #110

Earlier quoted context omitted.

> The model absolutely can be run at home. There even is a big community around running large models locally IMO 1tln parameters and 32bln active seems like a different scale to what most are talking about when they say localLLMs IMO. Totally agree there will be people messing with this, but the real value in localLLMs is that you can actually use them and get value from them with standard consumer hardware. I don't…

Local LLMs are just LLMs people run locally. It's not a definition of size, feature set, or what's most popular. What the "real" value is for local LLMs will depend on each person you ask. The person who runs small local LLMs will tell you the real value is in small models, the person who runs large local LLMs will tell you it's large ones, those who use cloud will say the value is in shared compute, and those who do…

> LLMs which the weights aren't available are an example of when it's not local LLMs, not when the model happens to be large.

I agree. My point was that most aren't thinking of models this large when they're talking about local LLMs. That's what I said, right? This is supported by the download counts on hf: the most downloaded local models are significantly smaller than 1tln, normally 1 - 12bln.

I'm not sure I understand what point you're trying to make here?

Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model

#149

Post by FuckButtons »

WhitneyLand wrote: post #129
johndough wrote: post #81

Earlier quoted context omitted.

The model absolutely can be run at home. There even is a big community around running large models locally: https://www.reddit.com/r/LocalLLaMA/ The cheapest way is to stream it from a fast SSD, but it will be quite slow (one token every few seconds). The next step up is an old server with lots of RAM and many memory channels with maybe a GPU thrown in for faster prompt processing (low two digits tokens/second). At t…

Its often pointed out in the first sentence of a comment how a model can be run at home, then (maybe) towards the end of the comment it’s mentioned how it’s quantized. Back when 4k movies needed expensive hardware, no one was saying they could play 4k on a home system, then later mentioning they actually scaled down the resolution to make it possible. The degree of quality loss is not often characterized. Which makes…

From my own usage, the former is almost always better than the latter. Because it’s less like a lobotomy and more like a hangover, though I have run some quantized models that seem still drunk.

Any model that I can run in 128 gb in full precision is far inferior to the models that I can just barely get to run after reap + quantization for actually useful work.

I also read a paper a while back about improvements to model performance in contrastive learning when quantization was included during training as a form of perturbation, to try to force the model to reach a smoother loss landscape, it made me wonder if something similar might work for llms, which I think might be what the people over at minimax are doing with m2.1 since they released it in fp8.

In principle, if the model has been effective during its learning at separating and compressing concepts into approximately orthogonal subspaces (and assuming the white box transformer architecture approximates what typical transformers do), quantization should really only impact outliers which are not well characterized during learning.

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