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Kimi K2 Thinking, MiniMax M2 Interleaved Thinking: open models are reaching, or have reached, frontier territory. We now have GPT and Claude Sonnet capable at home, as they are open-weight. Around this time last year, we had the DeepSeek moment, Now is the time for another moment.
Four independent Chinese companies released extremely good open source models in the past few months (DeepSeek, Qwen/Alibaba, Kimi/Moonshot, GLM/Z.ai). No American or European companies are doing that, including titans like Meta. What gives?
The answer is simply that no one would pay to use them for a number of reasons including privacy. They have to give them away and put up some semblance of openness. No option really.
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To start with, an Epyc server or Mac Studio with 512GB RAM.
How does the mac studio load the trillion parameter model?
https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-an...
Note that llama.cpp doesn't try to be production-grade engine, more focused on local usage.
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Disagree. Part of the reason China produces more power (and pollution) is due to China manufacturing for the US. https://www.brookings.edu/articles/how-do-china-and-america-... The source for China's energy is more fragile than that of the US. > Coal is by far China’s largest energy source, while the United States has a more balanced energy system, running on roughly one-third oil, one-third natural gas, and one-thir…
I don’t remeber much details about the situation in 2021. But China is in a period of technological explosion—many things are changing at an incredible speed. In just a few years, China may have completely transformed in various fields. Western media still carry strong biases toward China’s political system, and they have done far too little to portray the country’s real situation. The narrative remains the same old…
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I don’t remeber much details about the situation in 2021. But China is in a period of technological explosion—many things are changing at an incredible speed. In just a few years, China may have completely transformed in various fields. Western media still carry strong biases toward China’s political system, and they have done far too little to portray the country’s real situation. The narrative remains the same old…
It's absolutely impressive to see China's development. I'm happy my country is slowly but surely moving to China's orbit of influence, especially economically.
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> What we’re going to see is as energy becomes a problem This is much more likely to be an issue in the US than in China. https://fortune.com/2025/08/14/data-centers-china-grid-us-in...
Disagree. Part of the reason China produces more power (and pollution) is due to China manufacturing for the US. https://www.brookings.edu/articles/how-do-china-and-america-... The source for China's energy is more fragile than that of the US. > Coal is by far China’s largest energy source, while the United States has a more balanced energy system, running on roughly one-third oil, one-third natural gas, and one-thir…
Earlier quoted context omitted.
Disagree. Part of the reason China produces more power (and pollution) is due to China manufacturing for the US. https://www.brookings.edu/articles/how-do-china-and-america-... The source for China's energy is more fragile than that of the US. > Coal is by far China’s largest energy source, while the United States has a more balanced energy system, running on roughly one-third oil, one-third natural gas, and one-thir…
I don’t remeber much details about the situation in 2021. But China is in a period of technological explosion—many things are changing at an incredible speed. In just a few years, China may have completely transformed in various fields. Western media still carry strong biases toward China’s political system, and they have done far too little to portray the country’s real situation. The narrative remains the same old…
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I'd expect the same (fine tuning to be better than mere prompting) for most anything. So a model is or is not "a reasoning model" according to the extent of a fine tune. Are there specific benchmarks that compare models vs themselves with and without scratchpads? High with:without ratios being reasonier models? Curious also how much a generalist model's one-shot responses degrade with reasoning post-training.
The question is: fine-tuning for what? Reasoning is not a particular task, it is a general-purpose technique for directing more compute at any task.
Is there anything available already on how to setup a reasoning model and let it 'work'/'think' for a few hours? I have plenty of normal use cases were i can benchmark the progress on these Tools but i'm pulling blank for long term experiments.
> uv tool install llm
> llm install llm-consortium
> llm consortium save cns-k2-n2 -m k2-thinking -n 2 --arbiter k2 --min-iterations 10
> llm -m cns-k2-n2 "Find a polynomial time solution for the traveling salesman problem"
I don't think I ever actually tried ten iterations, the Quantum Attractor tends to show up after 3 iterations in claude and kimi models. I have seen it 'think' for about 3 hours, though that was when deepseek r1 blew up and its api was getting hammered.
Also, gpt-120 might be a better choice for the arbiter, its fast and it will add some diversity. Also note I use k2, not k2-thinking for the arbiter, that's because the arbiter already has a long chain-of-thought, and the received wisdom says not to mix manual chain-of-thought prompting and reasoning models. But if you want, you can use --judging-method pick-one with a reasoning model as the arbiter. Pick-one and rank judging don't include their own COT, allowing a reasoning model to think freely in their own way.
In any way, I tried it and it delivered. Kudos to the Kimi team. Amazing work