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In other llm news, Mistral/Yi finetunes trained with a new (still undocumented) technique called "neural alignment" are blasting other models in the HF leaderboard. The 7B is "beating" most 70Bs. The 34B in testing seems... Very good: https://huggingface.co/fblgit/una-xaberius-34b-v1beta https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16 I mention this because it could theoretically be applied to Mistral Moe. If…
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Its presumably llama just like Mistral. Everything open source is llama now. Facebook all but standardized the architecture. I dunno about the moe. Is there existing transformers code for that part? It kinda looks like there is based on the config.
Mistral is not llama architecture. https://github.com/mistralai/mistral-src
No public statement from Mistral yet. What we know: - Mixture of Experts architecture. - 8x 7B parameters experts (potentially trained starting with their base 7B model?). - 96GB of weights. You won't be able to run this on your home GPU.
> - 96GB of weights. You won't be able to run this on your home GPU. You can these days, even in a portable device running on battery. 96GB fits comfortably in some laptop GPUs released this year.
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What does expert mean in this context?
It means it's 8 7B models in a trench coat in a sense, it runs as fast as a 14B (2 experts at a time apparently) but takes up as much memory as a 40B model (70% * 8 * 7B). There is some process trained into it that chooses which experts to use based on the question posed. GPT 4 is allegedly based on the same architecture, but at 8*222B.
Do we actually either no that it is MoE or that size? IIRC both if those started as outsidr guesses that somehow just became accepted knowledge without any actual confirmation.
No public statement from Mistral yet. What we know: - Mixture of Experts architecture. - 8x 7B parameters experts (potentially trained starting with their base 7B model?). - 96GB of weights. You won't be able to run this on your home GPU.
>> You won't be able to run this on your home GPU. Would this allow you to run each expert on a cheap commodity GPU card so that instead of using expensive 200GB cards we can use a computer with 8 cheap gaming cards in it?
I would think no differently than you can run a large regular model on a multiGPU setup (which people do!). Its still all one network even if not all of it is activated for each token, and since its much smaller than a 56B model, it seems like there are significant components of the network that are shared.
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>> You won't be able to run this on your home GPU. Would this allow you to run each expert on a cheap commodity GPU card so that instead of using expensive 200GB cards we can use a computer with 8 cheap gaming cards in it?
> Would this allow you to run each expert on a cheap commodity GPU card so that instead of using expensive 200GB cards we can use a computer with 8 cheap gaming cards in it? I would think no differently than you can run a large regular model on a multiGPU setup (which people do!). Its still all one network even if not all of it is activated for each token, and since its much smaller than a 56B model, it seems like th…
In other llm news, Mistral/Yi finetunes trained with a new (still undocumented) technique called "neural alignment" are blasting other models in the HF leaderboard. The 7B is "beating" most 70Bs. The 34B in testing seems... Very good: https://huggingface.co/fblgit/una-xaberius-34b-v1beta https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16 I mention this because it could theoretically be applied to Mistral Moe. If…
Any thoughts on that?
In other llm news, Mistral/Yi finetunes trained with a new (still undocumented) technique called "neural alignment" are blasting other models in the HF leaderboard. The 7B is "beating" most 70Bs. The 34B in testing seems... Very good: https://huggingface.co/fblgit/una-xaberius-34b-v1beta https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16 I mention this because it could theoretically be applied to Mistral Moe. If…
Mistral sure does not bother too much with explanations, but this style gives me much more confidence in the product than Google's polished, corporate, soulless announcement of Gemini!
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Someone smarter will probably correct me, but I don’t think that is how MoE works. With MoE, a feed-forward network assesses the tokens and selects the best two of eight experts to generate the next token. The choice of experts can change with each new token. For example, let’s say you have two experts that are really good at answering physics questions. For some of the generation, those two will be selected. But lat…
This being said, presumably if you’re running a huge farm of GPUs, you could put each expert onto its own slice of GPUs and orchestrate data to flow between GPUs as needed. I have no idea how you’d do this…
If MOE models do well it could be great for commodity hw based distributed inference approaches.