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lopment of powerful AI, and would like to see more work on fleshing out and implementing these policies. ↩ For examples of recent work in this area, see the observational scaling laws described in Ruan, Maddison, and Hashimoto 2024 or the test-time compute scaling laws of Snell et al 2024 . ↩ In the same way that responsibly disclosing zero-day exploits does not mean immediately announcing the exploits as they are discovered, responsible disclosure does not necessarily mean immediately announcin

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rst, you can use OpenHands to directly call cloud APIs to bring up a machine. For instance, when I wanted a GPU machine to use I just said: As anyone who has done this through the AWS UI before can attest, this sort of thing requires a lot of clicks and can be a major time sink. Another option for performing deployments is to use infrastructure as code, where you specify the type of infrastructure you want as code, and execute the code to actually create the infrastructure. One common language f

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g Short-Term Memory. Neural Computation; Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020). Scaling Laws for Neural Language Models. arXiv preprint arXiv:2001.08361; Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781; Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in t

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ction applications (e.g., powering Amazon Rufus and LinkedIn AI features). Our bi-monthly meetups have become strategic gatherings for partnerships with industry leaders like IBM, AWS, and NVIDIA, marking our progress toward becoming the universal serving solution for the open-source AI ecosystem. Read on for more details about vLLM's 2024 achievements and 2025 roadmap! This blog is based on the 16th session of the bi-weekly vLLM Office Hours. Watch the recording here. Community Contributions an

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is usually enough. When and how to use frameworks There are many frameworks that make agentic systems easier to implement, including: The Claude Agent SDK ; Strands Agents SDK by AWS ; Rivet , a drag and drop GUI LLM workflow builder; and Vellum , another GUI tool for building and testing complex workflows. We suggest that developers start by using LLM APIs directly: many patterns can be implemented in a few lines of code. If you do use a framework, ensure you understand the underlying code. In

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hybrid patterns have demonstrated modest quality improvements, more efficient scaling to longer sequences, and faster inference (earlier this year, we reported the first scaling laws for hybrids). While new computational units and interconnection strategies enable a new frontier in model performance, they also present a key challenge to architecture design due to the vast number of possible designs. Rather than relying on manual optimization and heuristics applied to specific sub-classes of com

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cking examples in real life; Why does Reward Hacking Exist?; Hacking RL Environment; Hacking RLHF of LLMs Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards, without genuinely learning or completing the intended task. Reward hacking exists because RL environments are often imperfect, and it is fundamentally challenging to accurately specify a reward function. With the rise of language models generalizing to

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neralization: Unraveling Learning Differences Between Natural and Medical Images". In: arXiv preprint arXiv:2401.08865 (2024). [8] Yasaman Bahri et al. "Explaining neural scaling laws". In: arXiv preprint arXiv:2102.06701 (2021). [9] Utkarsh Sharma and Jared Kaplan. "A neural scaling law from the dimension of the data manifold". In: arXiv preprint arXiv:2004.10802 (2020). [10] Alessio Ansuini et al. "Intrinsic dimension of data representations in deep neural networks". In: Advances in Neural Inf

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the first album was released, Shaw and Deuce wasted no time with new material, releasing the "Beware Soul Snatchers" single within weeks of its appearance. An even denser slab of inboard studio trickery, "Soul Snatchers" was the clearest example to date of the group's evolving sound, though further recordings failed to materialize. ‍ 1 Leaving Perdition 8:10 2 Intermodal 3:39 3 Lunar White 3:25 4 She's A Black Belt 3:14 5 Rohm 1:09 6 Radiolucence (Version) 5:31 7 Deepest Laws 14:14 No comments:

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4) [8] Artom Eliseev and Denis Mazur, “Fast Inference of Mixture-of-Experts Language Models with Offloading,” arXiv preprint arXiv:2312.17238v1 [cs.LG], Dec 2023 (https://arxiv.org/pdf/2312.17238) [9] Leyang Xue, Yao Fu, Zhan Lu, Luo Mai, Mahesh Marina, “MoE-Infinity: Offloading-Efficient MoE Model Serving”, arXiv preprint arXiv:2401.14361, Jan 2024 [10] Tim Dettmers, Luke Zettlemoyer, “The case for 4-bit precision: k-bit Inference Scaling Laws”, arXiv preprint arXiv:2212.09720 [cs.LG], Dec 2022

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s, not just provide information, simplifying daily life for users. Finally, AI development must respect India's sovereignty, keeping data within the country and adhering to local laws. Vivek Raghavan, Co-Founder of Sarvam AI,said "We are thrilled to unveil our full-stack GenAI platform, which marks a pivotal moment for Sarvam AI and the Indian AI ecosystem. This launch is just the beginning of our journey. We will continue to enhance the capabilities of our platform with new features to ensure i

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the record labels have attempted to limit progress. They have spent decades attempting to control the terms of how we create and enjoy music, and this time is no different. This lawsuit is fundamentally flawed on both the facts and the law, and is nothing more than yet another instance where they chose litigation over innovation. For starters, the major record labels clearly hold misconceptions about how our technology works. Suno helps people create music through a similar process to one human

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ily life. We believe vLLM’s success comes from the power of the strong open source community. vLLM is actively maintai ned by a consortium of groups such as UC Berkeley, Anyscale, AWS, CentML, Databricks, IBM, Neural Magic, Roblox, Snowflake, and others. To this extent, we want to ensure the ownership and governance of the project is open an d transparent as well. We are excited to announce that vLLM has started the incubation process into LF AI & Data Foundation. This means no one party will ha

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o-use, open-source serving engine for large language models. vLLM has support for more than 40 types of open-source LLMs, a diverse set of hardware platforms (Nvidia GPU, AMD GPU, AWS Inferentia, Google TPU, Intel CPU, GPU, Gaudi, …) and all kinds of inference optimizations. Learn more about vLLM here. For the new Llama 3.1 series, vLLM can run the models with a full 128K context window. In order to support a large context window, vLLM automatically enables chunked prefill. Chunked prefill not o

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e assigned to an unemployed person by AMS, has become a literal textbook example of a biased decision-making algorithm within a governmental organization. There's also an ongoing lawsuit regarding that same algorithm (a biased linear regression model). It just really grinds my gears. Generative AI workshop As my rant about AMS Berufsinfomat got picked up by media and made the rounds on social media, a bunch of companies contacted me, asking for a workshop on generative AI. I've previously worked

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moon is. We can’t plot people and ChatGPT on a chart and draw a line to say when one will reach the other, even just extrapolating the current rate of growth. All analogies have flaws, and the flaw in my analogy, of course, is that if the Apollo program went wrong the downside was not, even theoretically, the end of humanity. A little before my grandfather, here’s another magazine writer on unknown risks: I was reading in the paper the other day about those birds who are trying to split the atom

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erywhere. There’s not necessarily any right answer here and no way to get to one through any analytic process - this is a social, cultural and political question, with all sorts of unpredictable outcomes. The US bans a gun database, and yet, the US also has a company that scans your driving licence against a private blacklist of tens of thousands of people (another database) shared across over thousands of bars and nightclubs. And no privacy laws. Ways to think about AGI Looking for AI use-cases

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r, is one of an alternative class of models called State Space Models ( SSMs ). Importantly, for the first time, Mamba promises similar performance (and crucially similar scaling laws ) as the Transformer whilst being feasible at long sequence lengths (say 1 million tokens). To achieve this long context, the Mamba authors remove the “quadratic bottleneck” in the Attention Mechanism. Mamba also runs fast - like “up to 5x faster than Transformer fast” 1 . Gu and Dao, the Mamba authors write: Mamba

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demand that Microsoft stop this from happening again. That would be the wrong level of abstraction. This, I think, is the challenging in talking about ‘AI Ethics’, or in writing laws to regulate ‘AI’. There are lots of ways that bad people can do bad things with software, and there are lots of ways that people can screw up with software - we worry what happens if the technology works and we worry what happens if it doesn’t work. In the last decade, machine learning created a bunch of new ways t

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rform, such as identification of the species or life stage of a fish. This may be especially challenging in a regulated industry like hydropower, as clients are subject to strict laws and regulations that require them to ensure that any tools or technologies they use are reliable and safe. They may be skeptical of a new machine learning system and may require assurances that it has been thoroughly tested and will not pose any risks to the environment or to through data integrity, algorithmic tra

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