搜索“Google”

t. The KV cache holds far less redundant capacity than the weights do, so compressing it is genuinely lossy. Angelos was candid about this from his own testing: the much-discussed Google method TurboQuant was announced as lossless but, in his experience, proved lossy in practice, because any change to the cache is hard for the model to recover from. The fix is again on the training side: distill the model so it grows accustomed to a lower-precision FP8 cache, and you keep most of the accuracy wh

匹配字段正文

and other concepts linked from the corresponding model cards and concept labels. For this tour of architecture advances and tweaks, we will go back to the beginning of April when Google released their new open-weight Gemma 4 suite of models. They come in 3 broad categories: the Gemma 4 E2B and E4B models for mobile and small, local (embedded) devices (aka IoT),; the Gemma 4 26B mixture-of-experts (MoE) model, optimized for efficient local inference,; and the Gemma 4 31B dense model, for maximum

匹配字段正文

f latency from replica spin-up. Managing both active instances and this buffer is a fun linear programming problem, as we’ve written elsewhere. Very roughly, it looks like: We use Google’s GLOP solver, feeding it scraped prices from cloud providers and tasks from users. Because cloud providers don’t always have capacity at prices and in regions that they advertise, we need to also feed back in the observed supply. Running a buffer limits the peak allocation utilization below 100%. This is a reas

匹配字段正文

roducts you actually use to talk to a model, and which let models do real work for you. The most common app is the website for each of these models: chatgpt.com, claude.ai, gemini.google.com (or else their equivalent application on your phone). Increasingly, there are other apps made by each of these AI companies as well, including coding tools like OpenAI Codex or Claude Code, and desktop tools like Claude Cowork. Harnesses are what let the power of AI models do real work, like a horse harness

匹配字段正文

ure, using DeepSeek V3 as a base model) helped DeepSeek become one of the most popular open-weight models and a legit alternative to proprietary models such as the ones by OpenAI, Google, xAI, and Anthropic. So, what’s new since V3/R1? I am sure that the DeepSeek team has been super busy this year. However, there hasn’t been a major release in the last 10-11 months since DeepSeek R1. Personally, I think it’s reasonable to go 1 year for a major LLM release since it’s A LOT of work. However, I saw

匹配字段正文

ace autoregressive LLMs. However, I can see them as interesting alternatives to smaller, on-device LLMs, or perhaps replacing smaller, distilled autoregressive LLMs. For instance, Google announced that it is working on a Gemini Diffusion model for text, where they state Rapid response: Generates content significantly faster than even our fastest model so far. And while being faster, it appears that the benchmark performance remains on par with their fast Gemini 2.0 Flash-Lite model. It will be i

匹配字段正文

ommitters and the UC Berkeley team. Each session covers recent updates, a deep dive with a guest speaker, and open Q&A. Join us every other Thursday at 2:00 PM ET / 11:00 AM PT on Google Meet, and get the recording and slides afterward on our YouTube playlist. Introduction Fast large language model (LLM) inference today requires executing models as efficiently as possible across diverse hardware, workloads, and scale. Efficient execution requires heavily optimized kernels that often require hand

匹配字段正文

ithin standard error, and the winner will depend heavily on hyperparameter tuning. Swish is used in most architectures today. However, GELU is not entirely forgotten; for example, Google's Gemma models still use GELU. What's more notable, though, is that the feed forward module (a small multi-layer perceptron) is replaced by a gated "GLU" counterpart, where GLU stands for gated linear unit and was proposed in a 2020 paper. Concretely, the 2 fully connected layers are replaced by 3 fully connecte

匹配字段正文

Claude now has Research capabilities that allow it to search across the web, Google Workspace, and any integrations to accomplish complex tasks. The journey of this multi-agent system from prototype to production taught us critical lessons about system architecture, tool design, and prompt engineering. A multi-agent system consists of multiple agents (LLMs autonomously using tools in a loop) working together. Our Research feature involves an agent that plans a research process based on user quer

匹配字段正文

guage Models with Optimal Planning Proficiency” arXiv preprint arXiv:2304.11477 (2023). [5] Yao et al. “ReAct: Synergizing reasoning and acting in language models.” ICLR 2023. [6] Google Blog. “Announcing ScaNN: Efficient Vector Similarity Search” July 28, 2020. [8] Shinn & Labash. “Reflexion: an autonomous agent with dynamic memory and self-reflection” arXiv preprint arXiv:2303.11366 (2023). [9] Laskin et al. “In-context Reinforcement Learning with Algorithm Distillation” ICLR 2023. [10] Karpas

匹配字段正文

e retrieved content as part of the prompt. The accuracy of such a process depends on the quality of both retrieval and generation steps. Lazaridou et al. (2022) studied how to use Google Search for document retrieval to augment LLMs. Given a question $q$, clean text is extracted out of 20 URLs returned by Google, resulting in a set of documents. Because these documents are long, each document is split into paragraphs of 6 sentences, $\{p\}$. Paragraphs are ranked by TF-IDF based cosine similarit

匹配字段正文