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PaperclipPerfector  7d ago (text post)   34971 thread views

Culture War Roundup for the week of January 20, 2025

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But more seriously, why is Facebook's Lllama so lousy by comparison if the labs are hiding their true edge? DeepSeek is presumably what they wish they had released and their AI team do not seem like dummies.

You've probably seen that bizarre teamblind thread. Meta is completely blindsided by DeepSeek. They are "moving frantically to dissect deepsek and copy anything and everything we can from it." It's pathetic.

Basically there's no secret: they suck and LLaMA sucks, it's a soft low-expectations research sinecure for people who want to publish papers and have weekends. Why did Timothée Lacroix and Guillaume Lample leave LLama team to found Mistral? And why did Mistral 7B destroy Llama-30B of the same generation (and currently mistral-123B is ≥ LLama-405B despite drastic difference in compute access)? Because they're better than that.

Llama is simply a bad yardstick. They dominate mindshare for reasons unrelated to their impressiveness. DeepSeek competes with industry leaders.

Wenfeng soon after founding DeepSeek V2, June 2024:

Liang Wenfeng: If the goal is just to make applications, then it is reasonable to follow the Llama architecture and start the product in a short period of time. But our goal is AGI, which means we need to research new model structure to realize stronger model capability with limited resources. This is one of the basic research that needs to be done to scale up to larger models. In addition to the model structure, we have done a lot of other research, including how to construct data, how to make the model more human-like, etc., which are all reflected in the models we released. In addition, Llama's architecture, in terms of training efficiency and reasoning cost, is estimated to be already 2 generations behind compared to the foreign state of the art. […] First of all, there is a gap in training efficiency. We estimate that compared to the best domestic or foreign level, the difference in model structure and training dynamics results in twice the compute cost for the same performance. In addition, there may also be another 2x gap in training data efficiency, that is, we need twice the training data to reach the same performance. Combined, that's four times more compute. What we're trying to do is to keep closing these gaps.

GPT-4o-mini is probably an 8b dense model. Frontier labs are efficient and have high margins. OpenAI and Anthropic are recouping their capex and exploiting captive audience. That's all.

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