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All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.

341
Stories
Исследования1 мин

On the Biology of a Large Language Model (Part 2)

Hello and welcome back to our analysis of on the biology of a large language model by anthropic. This is a blog post that anthropic has published uh investigating what they call the biology of a large language model. This works through uh a technique called attribution graphs.…

YouTube — Yannic Kilcher
Исследования1 мин

Energy-Based Transformers are Scalable Learners and Thinkers (Paper Review)

Hello, how's it going? Today we're going to look at this paper right here. Energy- based transformers are scalable learners and thinkers. This paper combines the uh general concept of energybased models with transformers and suggests a learning paradigm for it and shows how that…

YouTube — Yannic Kilcher
Исследования1 мин

Context Rot: How Increasing Input Tokens Impacts LLM Performance (Paper Analysis)

Hello, today we are looking at context rot how increasing input tokens impacts LLM performance. This paper looks at what happens if with these long context models now we are just sort of putting more and more stuff into the context of LLMs and even though all of these models do…

YouTube — Yannic Kilcher
Исследования1 мин

[Paper Analysis] The Free Transformer (and some Variational Autoencoder stuff)

Hello there. Today we are looking at the free transformer from France Fur at Farret Meta. This transformer is extending the classic decoderbased transformer with uh a series of latent variables that can be used to kind of make underlying decisions about the sequence to be…

YouTube — Yannic Kilcher
Исследования1 мин

泰坦:在考试时学习记忆(论文分析)

[哼哼]你好。今天我们来看看泰坦在考试时学习记忆的情况。这是 Google 研究的一篇论文,已作为其 Nurips 出版物的一部分推送。所以有一篇关于它的博客文章,人们在社交媒体上发布了关于它的文章,嗯,我不得不承认我……

YouTube — Yannic Kilcher
Исследования1 мин

TiDAR:在扩散中思考,在自回归中讨论(论文分析)

大家好,今天我们来看看 Nvidia 研究人员的 TiDaR 思维扩散讲座和自回归。这是一篇非常酷的论文,因为它有效地证明了您已经观察到我们在自回归大型语言模型期间没有充分利用 GPU……

YouTube — Yannic Kilcher
Исследования1 мин

Transformers, the tech behind LLMs | Deep Learning Chapter 5

The initials GPT stand for Generative Pretrained Transformer. So that first word is straightforward enough, these are bots that generate new text. Pretrained refers to how the model went through a process of learning from a massive amount of data, and the prefix insinuates that…

YouTube — 3Blue1Brown
Исследования1 мин

Attention in transformers, step-by-step | Deep Learning Chapter 6

In the last chapter, you and I started to step through the internal workings of a transformer. This is one of the key pieces of technology inside large language models, and a lot of other tools in the modern wave of AI. It first hit the scene in a now-famous 2017 paper called…

YouTube — 3Blue1Brown
Исследования1 мин

How might LLMs store facts | Deep Learning Chapter 7

If you feed a large language model the phrase, Michael Jordan plays the sport of blank, and you have it predict what comes next, and it correctly predicts basketball, this would suggest that somewhere, inside its hundreds of billions of parameters, it's baked in knowledge about…

YouTube — 3Blue1Brown
Исследования1 мин

Large Language Models explained briefly

[Submit subtitle corrections at criblate.com] Imagine you happen across a short movie script that describes a scene between a person and their AI assistant. The script has what the person asks the AI, but the AI's response has been torn off. Suppose you also have this powerful…

YouTube — 3Blue1Brown
Исследования1 мин

The AI that solved IMO Geometry Problems | Guest video by @Aleph 0

[Submit subtitle corrections at criblate.com] In January 2024, Google DeepMind released an AI model called Alpha Geometry, which could solve geometry problems from the International Mathematical Olympiad, or the IMO. The IMO is the highest level of competitive math contests at…

YouTube — 3Blue1Brown
Исследования1 мин

Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!

If you tell me what you like and what you don't like, I'll use that to train my model. Stack Quest. Hello, I'm Josh Starmer and welcome to Stack Quest. Today we're going to talk about reinforcement learning with human feedback and it's going to be clearly explained. This Stack…

YouTube — StatQuest AI
Исследования1 мин

Optimization with Linear Programming (and the Simplex Algorithm), Main Ideas!!!

You start with nothing, then you move. And you keep on moving till you can't get any more, then you're [music] done. StatQuest, bam. Hello, I'm Josh Starmer and welcome to StatQuest. Today, we're going to talk about optimization with linear programming main ideas. Specifically,…

YouTube — StatQuest AI
Исследования1 мин

The Simplex Algorithm, Mathematical Details!!!

We're going to find the optimal solution one row at a time. Stack Quest. Hello, I'm Josh Starmer and welcome to Stat Quest. Today we're going to talk about the simplex algorithm and we're going to go through the mathematical details. This stack quest is brought to you by the…

YouTube — StatQuest AI
Исследования1 мин

优化 ARM CPU 上的专家混合 LLM 推理

大家好,我是来自英特尔印度研究院的 Majid,我很高兴能在今年 10 月的 PyTorch 北美会议上发表演讲。我将讨论使用 VLLM 和 OpenVINO 的专家 LLM 推理的有效组合,以及我们如何在 ARM CPU 上高效运行这些模型。如果…

YouTube — PyTorch Official
Исследования1 мин

PyTorch-Native LLM Serving on TPU: SGLang and vLLM

Hi, I'm G from Google. >> I'm Colin from Meta. >> I'm Angela from Meta. >> We are going to be speaking at the PyTorch conference San Jose about new VLM and SG learn engines built on a new PyTorch native TPU backend torch TPU. >> Rather than build a new serving engine or porting…

YouTube — PyTorch Official
Исследования1 мин

Can we hear disease before we see it?

Интересно представить, что будущее клинической помощи может заключаться в том, что мы будем слышать, прежде чем видеть. В исследовательском центре Google мы разработали акустические модели для здравоохранения, известные как Here, которые являются базовой моделью. С помощью Here…

YouTube — Google Research
Стартапы1 мин

Open Source Inference at Production Scale

Hello everyone, I'm Carlos Costa, a distinction engineer here at IBM Research and co-founder of the LMD project. I'll be heading soon to San Jose for PyTorch Conference North America, where I'll have the privilege of sharing some exciting news about LMD. How the project changed…

YouTube — PyTorch Official
Исследования1 мин

How Google Research Is Accelerating Scientific Discovery | The Google Research Podcast

When you look at the history of humanity, you can see just how important scientific discovery has been. From the invention of the microscope and the discovery of bacteria to the invention of antibiotics. So I want to understand what will artificial intelligence mean for science.…

YouTube — Google Research
Исследования1 мин

Multi-Agent AutoResearch with Open Source Models

Hi, in this video I'm going to walk you through a multi-agent setup of AutoResearch. I use open source models and an open source code harness, OpenCode, and I implement AutoResearch, which is a project by Andre Carpathie where he takes a nano GPT project, an LLM in its simplest…

YouTube — Hugging Face
Исследования1 мин

How to Create an LLM Dataset | FineWeb Overview

Good morning everyone. How's it going today? Today we're going to be talking about the fine web data set. Now, this is a project by Hugging Face and it is a great example of how to gather all the data that you need to train a large language model. This actually is also available…

YouTube — Hugging Face
Исследования1 мин

Hugging Face Journal Club: AsyncOPD and How Stale Can On-Policy Distillation Be?

This paper is doing something if I understand correctly that is very similar to what pipeline RL proposed for reinforcement learning which is where you want to basically have like really decoupled generators from learners from trainers um but you have the additional complexity…

YouTube — Hugging Face
Исследования1 мин

The Hugging Face Hub for Enterprise & Academia

Всем привет. Если вы работаете с ИИ, у вас наверняка уже есть аккаунт на Hugging Face Hub, где удобно демонстрировать свои модели, скачивать модели с открытым кодом, использовать публичные наборы данных и создавать демоверсии своих моделей. Но публичный хаб—это только начало. В…

YouTube — Hugging Face
Исследования1 мин

Hugging Face Journal Club: Kimi K3

Let [clears throat] me think where to start. Okay, so we've got the Kim K3 report and it's quite a beast in terms of details, which is pretty awesome. And the bits that I focused on were really just the the pre- uh sorry, the post-training parts. And even that I wasn't able to…

YouTube — Hugging Face
Исследования1 мин

Hugging Face Journal Club: Scaling Laws for Pre-training & RL

Uh, this paper is actually quite interesting. It dives into something that that has been I guess a question an open question for the Ember communities the Um, we can call them like scaling laws for post-training. And then how is pre-training allocation of compute impact…

YouTube — Hugging Face
Исследования1 мин

Hugging Face Journal Club: Direct On-Policy Distillation

Okay, so this is quite um interesting paper and a bit uh surprising that uh how it works, but uh basically the idea is that uh in post-training, you have usually uh a pipeline where you have a model and then you uh run RL over that model. And ideally that uh post-RL model should…

YouTube — Hugging Face
Исследования1 мин

Optimize, deploy, and benchmark an open-source LLM with vLLM

I'm thrilled to introduce this course on fast and efficient LLM inference, built in partnership with Red Hat, and taught by Sergey Kliger. Deploying open source LLMs efficiently, so they can serve many users at once with low latency and reasonable cost, is challenging. In this…

YouTube — DeepLearning.AI