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ase continue sharing your feedback, bug reports, and feature requests! Acknowledgments Thanks to all our private alpha testers: Anthropic, Apollo Research, Bridgewater AIA Labs, Epoch AI, Google DeepMind, HAL (Princeton Language and Intelligence), MATS Research, METR, Palisade Research, Penrose, Redwood Research, Thinking Machines Lab, UIUC DDKang Lab, as well as other unlisted organizations and individuals. Please reach out to join our community of alpha testers and receive white-glove support!

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g large documents, Ollama’s context length can be increased in the settings. Note: this will require more memory. Multimodal support Building on Ollama’s new multimodal engine, images can be sent to models that support them, such as Google DeepMind’s Gemma 3 model. Documentation writing Code files can be processed by models for understanding. Download Ollama’s new app today on macOS and Windows. For pure CLI versions of Ollama, standalone downloads are available on Ollama’s GitHub releases page.

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e optimizations to improve how memory can be efficiently used. Ollama configures causal attention at the individual model level instead of configuring as a group. Examples: Google DeepMind’s Gemma 3 leverages sliding window attention, and Ollama can leverage that to allocate a subset or a portion of the model’s context length to improve performance, and because of the resulting memory efficiency, we can increase the context length of the model on the same system or use the remaining memory for h

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runtime of operations on GPUs to analyze bottlenecking factors during inference workloads on a single node. Much of the notation is borrowed from How To Scale Your Model by Google DeepMind. If you are already familiar with traditional inference math, skip to Distributed Inference. Accelerator Math Whenever we run an operation on an accelerator, there are two things that take time: Computation: How long it takes to compute all the floating-point operations (FLOPs) required for the operation Commu

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ai-models-dangerous-before-public-deployment/}} , year = {2025} , month = {01} , } Many frontier AI safety policies from scaling labs (e.g. OpenAI’s Preparedness Framework, Google DeepMind’s Frontier Safety Framework, etc.), as well as past work by third party evaluators including UK AISI , Apollo Research , and METR , focus on pre-deployment testing – ensuring that the AI model is safe and that the lab has sufficient security before the lab deploys the model to the public. Such pre-deployment s

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yone to contribute compute and participate. This brings us one step closer towards open source AGI. Recently, we published OpenDiLoCo, an open-source implementation and scaling of DeepMind’s Distributed Low-Communication (DiLoCo) method, enabling globally distributed AI model training. We not only replicated and open sourced this work but successfully scaled it to the 1B parameter size. Now, we are scaling it up by a further 10× to 10B-parameter model size, 25x from the original research. This b

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and reward progress. To tie these ideas about measurement together, we suggest a taxonomy, looking at measures of AI capabilities, behaviors, usage, and impacts . Similar to this DeepMind paper , the idea is to examine spheres of expanding context, from testing a model in isolation (both what it is capable of and what behaviors it demonstrates), all the way to understanding what happens when a model meets the real world (how humans use it, and what its impact is on them and society). The idea i

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，并且处理器是使用基于这些算法构建的定制执行数据集来训练的。这通常既需要领域专业知识，也需要计算机科学专业知识，因此构成了明显的入门门槛！ 自从我开始从事NAR以来，我一直对降低这一入门门槛抱有浓厚兴趣，同时也希望提供一组在各种任务中都应该有用的“基础处理器”。这促成了为期两年的工程努力，最终开源了CLRS算法推理基准（https://github.com/deepmind/clrs）[44]。 CLRS基准的灵感来自Cormen、Leiserson、Rivest和Stein的经典教材《算法导论》（CLRS）[1]，这是关于经典算法和数据结构最受欢迎的本科教材之一，也是竞赛程序员的“圣经”。尽管它有数千页之多，却只包含约90种不同的算法，而这些算法往往构成了整个软件工程职业生涯的基础！因此，CLRS中的算法可以构成我们所需的坚实的“基础处理器”集合，我们着手让构建和训练NAR处理器来执行CLRS中的算法变得容易。 在其第一个版本中，我们发布了CLRS-30，这是CLRS中三十种算法的数据集和处理器生成器集合，涵盖多种技能：排序、搜索、动态规划、图算法、字符串算法和几何算法。 CLRS-

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tion retrieval. (blog post); Improving language models by retrieving from trillions of tokens (2021): RETRO, for “Retrieval Enhanced TRansfOrmers,” is another approach—this one by DeepMind—to improve LLM accuracy by accessing information not included in their training data. (blog post); LoRA: Low-rank adaptation of large language models (2021): This research out of Microsoft introduced a more efficient alternative to fine-tuning for training LLMs on new data. It’s now become a standard for commu

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estion-answer pairs authored by trivia enthusiasts and independently gathered multiple evidence documents per question; Jeopardy! Questions: contains 200,000+ Jeopardy! questions; DeepMind Q&A Dataset: question/answer pairs from CNN and Daily Mail articles; bAbi: a rich collection of datasets for text understanding by Facebook; FEVER: for fact extraction and verification; SearchQA: question-answer pairs were crawled from from J! Archive, and then augmented with text snippets from Google; Quasar-

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y discourages revisiting the same state within the same episode; Slowly discourages revisiting states that have been visited many times across episodes Later, built on top of NGU, DeepMind proposed “Agent57” (Badia, et al. 2020b), the first deep RL agent that outperforms the standard human benchmark on all 57 Atari games. Two major improvements in Agent57 over NGU are: A population of policies are trained in Agent57, each equipped with a different exploration parameter pair $\{(\beta j, \gamma j

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athcal{L}(B) = \sum {(\mathbf{x} i, \mathbf{x} j, y i, y j)\in B} (r {ij} - \mathbf{1} {y i=y j})^2$ (Note: There is another Relation Network for relational reasoning, proposed by DeepMind. Don’t get confused.) Prototypical Networks Prototypical Networks (Snell, Swersky & Zemel, 2017) use an embedding function $f \theta$ to encode each input into a $M$-dimensional feature vector. A prototype feature vector is defined for every class $c \in \mathcal{C}$, as the mean vector of the embedded support

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rsarial Networks.” arXiv preprint arXiv:1805.08318 (2018). [12] Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel. “A simple neural attentive meta-learner.” ICLR 2018. [13] “WaveNet: A Generative Model for Raw Audio” - Sep 8, 2016 by DeepMind. [14] Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. “Pointer networks.” NIPS 2015. [15] Alex Graves, Greg Wayne, and Ivo Danihelka. “Neural turing machines.” arXiv preprint arXiv:1410.5401 (2014). Architecture; Attention; Transformer; Rnn

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e of Go has been an extremely hard problem in the field of Artificial Intelligence for decades until recent years. AlphaGo and AlphaGo Zero are two programs developed by a team at DeepMind. Both involve deep Convolutional Neural Networks (CNN) and Monte Carlo Tree Search (MCTS) and both have been approved to achieve the level of professional human Go players. Different from AlphaGo that relied on supervised learning from expert human moves, AlphaGo Zero used only reinforcement learning and self-

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