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2024 · arXiv
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.
Metadata: arXiv. Abstracts retain their original authors’ rights.
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9 selected excerpts · 9 total mentions
1e21 flops is hilariously wrong. for reference the llama 3 8B model (https://arxiv.org/pdf/2407.21783) used 10 times that many flops. This model is 350x bigger in total params and 12x bigger in active params and was trained on 3x the data.
Meta synthetically generated lots of PHP from Python for Llama 3 for training purposes. Meta writes a crazy amount of PHP internally. Translation tends to be way easier than unconstrained generation for LLMs. But if you can translate and filter a large amount of code, you can learn to generate. If you also translate and run the unittests, you get another layer of error checking. https://arxiv.org/abs/2407.21783 See figure 8.
Right, the original LLaMA paper. LLaMA 2 and 3 are significantly more capable models trained on orders of magnitude more data, and those papers notably do not say where the data comes from. The LLaMA 3 paper helpfully mentions that "Much of the data we utilize is obtained from the web," so I guess that's better than nothing! LLaMA 2: https://arxiv.org/pdf/2307.09288 LLaMA 3: https://arxiv.org/pdf/2407.21783
> Which shows that "massive scaling", even enormous, gigantic scaling, doesn't improve intelligence one bit; it improves scope, maybe, or flexibility, or coverage, or something, but not "intelligence". Do you have any data to support 1. That grok is not more intelligent than previous models (you gave one anecdotal datapoint), and 2. That it was trained on more data than other models like o1 and Claude-3.5 Sonnet? All datapoints I have support the opposite: scaling actually increases intelligence of models. (agreed, calling this "intelligence" might be a stretch, but alternative definitions like "scope, maybe, or flexibility, or coverage, or something" seem to me like…
beating around the bush to avoid saying that machines have intelligence) Check out the technical report of Llama 3 for instance, with nice figures on how scaling up model training does increase performance on intelligence tests (might as well call that intelligence): https://arxiv.org/abs/2407.21783
Yes, they can. Meta described a clever way in their paper on training Llama3 [1] (in section about factuality). The idea is to sample several answers to a question that you know the answer to. Let an LLM decide if the given answers are different from your known truth. If so, you found a question, that you can train your LLM in the next post-training round to answer with "I don't know". Do that a couple hundred of times and your LLM will identify neurons that indicate doubt and from here on have the ability to answer with "I don't know". [Edit] The article here also mentions a paper [2] that comes up with the idea of an uncertainty token. So here the incorporation of uncertainty is already baked in at pre-training.[/Edit] [1] https://arxiv.org/pdf/2407.21783 [2] https://openreview.net/pdf?id=Wc0vlQuoLb
Facebook did a technical paper where they described their training cluster and the sheer amount of complexity is staggering. That said, the original poster was interested in inferencing, not training. https://arxiv.org/pdf/2407.21783
Thanks for sharing your thoughts. I have a somewhat more optimistic take: We will create some form of artificial intelligence (AI) in the next decade. However, the recent (i.e. over last five years) advances in natural language processing are not the ones that automatically lead to that. I think we need more fundamental breakthroughs than attention-based neural networks and all the tricks that have gone into say a Llama3. [1] Even if we did develop an AI more aggressively, say by 2030, I am not sure if that automatically leads to extinction. Here, my optimism is guided by the last few times we had major technological innovations (i.e. Steam Engine, Rise of the…
Semiconductor based circuits, etc.). To be sure, these technologies were disruptive, probably net-negative in terms of pure climate impact and led to near-term job losses and generally helped increase the power of people on the top of the capitalist food-chain. However, they also helped us accelerate into a world where we can produce more food than we need for feeding everybody on the planet in a given year. And generally, helped us see more of the world by connecting the planet both physically and virtually. I am not a blind optimist though. I think we need regulation for harm prevention. The problem with that approach is that we need to understand what harms are being caused first and in some instances that might be too late. But, I don't think we start by taking a blanket position (as a society/polity) on how technological innovation should occur -- because a) we are not there yet and b) that's just a poor way to do science. [1] https://arxiv.org/pdf/2407.21783
GPT-4o has seen a lot of examples of what people write about coding on the web, what code exists, and what tasks people want to do with that. But that general data doesn't include full process of coding - get a bug report, look at a codebase beforehand, make changes to the codebase, test them and see exactly what bugs they have, and iterate. That process is what SWE-bench tests. It's possible OpenAI did some coding fine-tuning themselves; Meta's Llama 3 paper [0], section 4.3.1 mentions what sort of work is needed. However, anything OpenAI did is based on their own tooling and set of assumptions - e.g. how is the existing code input into the LLM, what set of actions can…
the LLM take (e.g. look up documentation), what language is the output code being written in, etc. Cosine's LLM framework may do things differently and have different features, so you'd need to fine-tune the LLM to take maximum advantage of the framework. It's like dropping the LLM down in front of Vim when it had only ever used or even heard of notepad (or even emacs); there needs to be some training to make it work well with the new tools it has. [0]: https://arxiv.org/pdf/2407.21783
I haven’t tried Llama 3.1 405B much yet, but I was impressed by the detail and openness of Meta’s paper about it [1]. This interview with Meta’s head of product management for generative AI adds some interesting perspective. [1] https://arxiv.org/abs/2407.21783
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