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How Transformers.js Works: AI Models in JavaScript, Explained

Transformers.js brings state-of-the-art machine learning to JavaScript. You can think of it as a library for running AI models locally in the browser through a single high-level API that handles model loading, pre-processing, inference, and post-processing. Hi, I'm Nico and in…

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
Стартапы1 мин

Что можно построить во время 48-часового хакатона по робототехнике?

Год назад тысячи людей по всему миру потратили один уик-энд на создание роботов, которых они никогда раньше не создавали. Вот что они сделали и где они сейчас находятся. >> Привет, я Никодим из Hugging Face. Ровно год назад хакатон Robot Worldwide собрал тысячи…

YouTube — Hugging Face
База знаний1 мин

AI Dev 26 x SF | Paul Everitt: The Shift to Agentic Engineering

Today we are going to hear about the shift towards agentic engineering and increased code output. Welcome Paul. >> One minute into this conference Jonathan said Let's talk about the future of software engineering. Let's have a conversation. Let's figure it out. That's exactly…

YouTube — DeepLearning.AI
База знаний1 мин

Train AI Robots Without Writing Code! (Introducing LeLab)

This is Leloup, a graphical user interface for the Robot Library. It lets you teleoperate the robots, collect the data set, train models both on your own hardware and with powerful GPUs available through hugging face. And finally, you can of course deploy your working policies…

YouTube — Hugging Face
База знаний1 мин

Hermes Architecture EXPLAINED: Memory, Context & Gateways

Good morning everyone. How's it going today? Uh today we're going to be taking a look at the Hermes agent and its architecture. So, we're going to be covering a pretty high-level overview of how this agent is built so that you understand a little bit better how to use it and…

YouTube — Hugging Face
База знаний1 мин

Introducing Tau: An Educational Coding Agent

Good morning, everyone. How's it going? Today, we're going to be introducing Tao, which is a coding agent that is designed to be educational and to help you build your own coding agents. So, the objective of this project is not to make it compete with established agents like Pi…

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
База знаний1 мин

AI Dev 26 x SF | Or Dagan: Optimizing Accuracy, Cost, and Latency in Real-World Agents

We're trying to optimize that uh vicious cycle of accuracy, cost, latency, uh and you know, it always feels like you can't really have all three. Uh you optimize for one, add another one, the the last one drops. So, that would be what I will be talking about today. How can we…

YouTube — DeepLearning.AI
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
База знаний1 мин

AI Dev 26 x SF | Daniel Beutel: Flower SuperGrid Agents

I'm here today to talk about a new frontier that we see opening in AI. That frontier is collaborative AI systems. Um that frontier is going to unlock a new category of systems to be built. Systems that were not possible before. But before I do that, I just want to say a few…

YouTube — DeepLearning.AI
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
База знаний1 мин

Tau Crash Course: The Python Port of Pi

So, recently it has become very clear that coding harnesses are one of the most important parts when creating reliable AI workflows. And what we're going to be doing today is we're going to be taking a look at one coding harness that we're developing internally right here called…

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
База знаний1 мин

AI Dev 26 x SF | Andi Partovi: Why Every Agent Needs a Simulation Sandbox

So, I'm Andy. I'm the CTO and co-founder of Verifai. Um I'm going to talk about simulation environments for agents. Now, I'm assuming you are here because you're very interested in agents, but I also do want to see a show of hands, who here is building AI agents, not just using…

YouTube — DeepLearning.AI
База знаний1 мин

Prompt Caching Explained: Stop Overpaying for AI Agents

So, let's suppose that you're working with your coding agent and you already have accumulated 50k tokens of context. And then you send an additional 1k tokens for an additional query, an additional question that you send to it. So, that adds a full context of 51,000 tokens. Are…

YouTube — Hugging Face
База знаний1 мин

AI Dev 26 x SF | Ara Khan: Evals Are Broken Use Them Anyway

I'm Era. I'm going to be talking about emails, specifically AI emails like coding agent emails and stuff. And I'm going to talk about how they're broken and how you could still use them anyway. Uh before I start, I just I want to say one thing. It's just like it boggles my mind…

YouTube — DeepLearning.AI
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
База знаний1 мин

Agent Memory EXPLAINED - Complete Architecture

Good morning everyone. If you want your agent to remember things across conversations about yourself, about itself, and what it learns about the world, you're going to need an agent memory system. And today, that's what we're going to be explaining. First of all, we're going to…

YouTube — Hugging Face
База знаний1 мин

Build Your Own App In Just 30 Minutes! Full Course with Andrew Ng

Hi, I'm Andrew Ng. I want to show you how to build fun, useful software using AI, regardless of your background or training. If you're intrigued about using AI or live coding to build cool software, but aren't sure where to start, this course is for you. Let me show you a funny…

YouTube — DeepLearning.AI
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
Стартапы1 мин

Hugging Face Journal Club: Training AI Scientists to Replicate Research

All right. So this paper um is about a model called Faraday um which was trained by this new startup called inherent and what they train the model to do is to do a very narrow part of like scientific replication. So if I understood correctly, basically they want to take a bunch…

YouTube — Hugging Face
Исследования1 мин

Hugging Face Journal Club: AI Research Preference Models

Yeah, I think it's a really cool paper. Um, I think we've talked a bit about this before or like I think it has been a bit in the in the common discourse that like now that implementation is sped up so much, um, like the the value that researchers can provide is more in their…

YouTube — Hugging Face
База знаний1 мин

Fast inference changes what you can build

Cerebras, which I've been a fan of for a long time, is one of the leaders of fast inference. I'm excited to introduce this short course on fast LM inference built in partnership with Cerebras and taught by Jem Weigall, Satya, and Sara Hooker. Fast inference make new classes of…

YouTube — DeepLearning.AI
Исследования1 мин

We shipped 207 WebGPU Kernels for Browser AI

We just shipped more than 200 WebGPU kernels for browser AI and they are all available open source on the Hugging Face Hub. We also shipped a new library called Hugging Face Kernels that allows you to load and to execute all of those kernels. Let's have a look at this little…

YouTube — Hugging Face