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New short course: Open Source Models with Hugging Face 🤗, taught by Maria Khalusova, Marc Sun, and Younes Belkada! Hugging Face has been a game changer by letting you quickly grab any of hundreds of thousands of already-trained open source models to assemble into new applications. This course teaches...

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Andrew Ng

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224,520 次观看 • 2 年前 •via X (Twitter)

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Leandro von Werra 的头像
Leandro von Werra2 年前

@mariaKhalusova @_marcsun @huggingface The one and only @younesbelkada!

Thomas Wolf 的头像
Thomas Wolf2 年前

@mariaKhalusova @_marcsun @huggingface dream team 🤩

race 的头像
race2 年前

@mariaKhalusova @_marcsun @huggingface Where do I get one of those shirts

Dankoyy 的头像
Dankoyy2 年前

I really liked the course, but I believe it could be multilingual. When we talk about AI, courses on the themes could easily be translated into almost any language with extreme quality. This includes teaching the AI specific words that don't need translation. Advancing AI is also about reaching the most vulnerable and transforming their lives through the enhancement of productive capacity.

Suzana Ilić 的头像
Suzana Ilić2 年前

@mariaKhalusova @_marcsun @huggingface Younes! amazing go go go!! 🔥 @younesbelkada

Nova Lead 的头像
Nova Lead2 年前

@mariaKhalusova @_marcsun @huggingface Thrilled to see @huggingface leading the charge with their new course on Open Source Models! The power of collaboration and open-source innovation is truly transforming AI. Can't wait to explore the synergies between these models and our initiatives. The future of AI is bright

Arvind Nagaraj 的头像
Arvind Nagaraj2 年前

@mariaKhalusova @_marcsun @huggingface This is such a wonderful 🤗 course - nice to see multimodality get coverage! And so cool to see @younesbelkada code live! If you wish to understand multimodality in depth, please see my blog posts: 1. 2.

Matteo Troìa 的头像
Matteo Troìa2 年前

@mariaKhalusova @_marcsun @huggingface @Alessio_Zoccoli 😉😎

Wambugu Muchemi 🔬 的头像
Wambugu Muchemi 🔬2 年前

@mariaKhalusova @_marcsun @huggingface I love the course. Well taught and insightful. Asante!

Toronto Consulting Group 的头像
Toronto Consulting Group2 年前

@mariaKhalusova @_marcsun @huggingface Wawawiwa!

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LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's @younesbelkada and Marc Sun, you'll: - Learn how to quantize nearly any open source model - Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library - Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here:
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LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's @younesbelkada and Marc Sun, you'll: - Learn how to quantize nearly any open source model - Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library - Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here:

Andrew Ng

288,266 次观看 • 2 年前

New short course: Building Multimodal Search and RAG", by Weaviate AI Database's Sebastia(N_) Witalec ✊🏽✊🏾✊🏿. Contrastive learning is used to train models to map vectors into an embedding space by pulling similar concepts closer together and pushing dissimilar concepts away from each other. This technique is also used to train multimodal embedding models that capture semantic similarity across different modalities like text, images, and audio. These multimodal embeddings can be used to build multimodal search and RAG systems. In this course, you'll learn how contrastive learning works, and how to add multimodality to RAG – so your models can draw on diverse, relevant context to answer questions. For example, a query about a financial report might synthesize information from text snippets, graphs, tables, and slides. You will also learn how visual instruction tuning lets you integrate image understanding into language models, and build a multi-vector recommender system using Weaviate’s open-source vector database. Please sign up here:
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New short course: Building Multimodal Search and RAG", by Weaviate AI Database's Sebastia(N_) Witalec ✊🏽✊🏾✊🏿. Contrastive learning is used to train models to map vectors into an embedding space by pulling similar concepts closer together and pushing dissimilar concepts away from each other. This technique is also used to train multimodal embedding models that capture semantic similarity across different modalities like text, images, and audio. These multimodal embeddings can be used to build multimodal search and RAG systems. In this course, you'll learn how contrastive learning works, and how to add multimodality to RAG – so your models can draw on diverse, relevant context to answer questions. For example, a query about a financial report might synthesize information from text snippets, graphs, tables, and slides. You will also learn how visual instruction tuning lets you integrate image understanding into language models, and build a multi-vector recommender system using Weaviate’s open-source vector database. Please sign up here:

Andrew Ng

104,371 次观看 • 2 年前

Welcome to the party Hugging Face 🤗 Access the Hugging Face Hub directly from Gemini CLI with this new Gemini CLI extension. 🔍 - Search models, datasets and papers 📈 - Find trending models or datasets 🤗 - Learn how to fine-tune models and more! Big thanks to Shaun Smith and Vaibhav (VB) Srivastav for making this happen!
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Welcome to the party Hugging Face 🤗 Access the Hugging Face Hub directly from Gemini CLI with this new Gemini CLI extension. 🔍 - Search models, datasets and papers 📈 - Find trending models or datasets 🤗 - Learn how to fine-tune models and more! Big thanks to Shaun Smith and Vaibhav (VB) Srivastav for making this happen!

Jack Wotherspoon

14,481 次观看 • 7 个月前

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"Introducing Multimodal Llama 3.2": As promised two weeks ago, here's the short course on Meta's latest open model! This short course is created with Meta and taught by Amit Sangani, Director of AI Partner Engineering at Meta. Meta’s Llama family of models is leading the way in open models, allowing anyone to download, customize, fine-tune, or build new applications on top of them. Learn about the vision capabilities of the Llama 3.2, and use it for image classification, prompting, tokenization, tool-calling. You'll also learn about the open-source Llama stack, which gives building blocks for many different stages of the LLM application life cycle. In detail, you’ll: - Learn what are the features of Meta's four newest models, and when to use which Llama model. - Learn best practices for multimodal prompting, with applications to advanced image reasoning, illustrated by many examples: Understanding errors on a car dashboard, adding up the total of photographed restaurant receipts, grading written math homework. - Use different roles—system, user, assistant, ipython—in the Llama 3.1 and 3.2 models and the prompt format that identifies those roles. - Understand how Llama uses the tiktoken tokenizer, and how it has expanded to a 128k vocabulary size that improves encoding efficiency and multilingual support. - Learn how to prompt Llama to call built-in and custom tools (functions) with examples for web search and solving math equations. - Learn about Llama Stack, a standardized interface for common toolchain components like fine-tuning or synthetic data generation, useful for building agentic applications. By the end of this course, you’ll be equipped to build out new applications with the new Llama 3.2. Thank you to Ahmad Al-Dahle, Amit Sangani, and the whole AI at Meta team AI at Meta for all the hard work on Llama 3.2 — we’re excited to make these open models even more accessible to more developers with this new course! Please sign up here!

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131,606 次观看 • 1 年前

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In Prompt Engineering for Vision Models, taught by Abby Jacques Verre and Caleb Kaiser of Comet , you’ll learn how to prompt and fine-tune vision models for personalized image generation, image editing, object detection and segmentation. The prompts you'll use for vision models could be text, point coordinates, or bounding boxes, depending on the model. You'll also learn to tune hyperparameters to shape the output. Models you'll use include Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. You'll also learn to fine-tune Stable Diffusion to generate personalized images (say, an image of a specific person), using a handful of images for training. As an example of a multi-step workflow, you'll use OWL-ViT to detect an object based on a text prompt, then pass the bounding box to SAM to create a segmentation mask, and input that mask into Stable Diffusion to replace the original object with a new one based on a text prompt. Controlling vision models can be tricky; this course will teach prompting and fine-tuning techniques to get precise control over their output. Get started here:

Andrew Ng

151,198 次观看 • 2 年前

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Andrew Ng

162,798 次观看 • 2 年前

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Explore state-of-the-art multimodal prompting in our new short course Large Multimodal Model Prompting with Gemini, taught by Erwin Huizenga in collaboration with Google Cloud. One interesting insight from this course: with multimodal models, prompt structure matters significantly. Placing text inputs, such as a patient's medical history, before image inputs, like an X-ray, can enhance the model's ability to contextualize and interpret visual data effectively. In other contexts, such as image captioning, you may get better results by putting the image first. Multimodal models behave differently than text-only LLMs, and effective prompting for models varies depending on the model you’re using. In this course you’ll learn how to effectively prompt Gemini models. Gemini's multimodal capabilities also enable new approaches in AI application development, for example: - The Gemini library handles various video formats (MP4, MOV, MPEG), streamlining applications using these formats. - Large context window (up to 1 million tokens) enables processing of extensive content, like analyzing multiple 50-minute videos simultaneously. - Function calling feature integrates real-time data (e.g., current exchange rates) into model responses. The course demonstrates building multimodal applications with real-world examples including document analyzers that reason across text and graphs simultaneously, video content extractors that find and timestamp specific information from multiple hours of footage, and automated expense report systems processing receipt images while cross-referencing company policies. Sign up here:

Andrew Ng

73,915 次观看 • 1 年前

PAIR-Diffusion: Object-Level Image Editing with Structure-and-Appearance Paired Diffusion Models Gradio demo is out on Hugging Face Spaces demo:
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PAIR-Diffusion: Object-Level Image Editing with Structure-and-Appearance Paired Diffusion Models Gradio demo is out on Hugging Face Spaces demo:

AK

87,679 次观看 • 3 年前

Introducing Poe Apps: a new, easy way to create and use visual interfaces into any combination of the 100+ text, image, video, and audio models on Poe. (1/5)
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Introducing Poe Apps: a new, easy way to create and use visual interfaces into any combination of the 100+ text, image, video, and audio models on Poe. (1/5)

Poe

66,708 次观看 • 1 年前

try out the Gradio Demo for AudioLDM: Text-to-Audio Generation with Latent Diffusion Models on Hugging Face demo:
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try out the Gradio Demo for AudioLDM: Text-to-Audio Generation with Latent Diffusion Models on Hugging Face demo:

AK

82,137 次观看 • 3 年前

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Introducing "Building with Llama 4." This short course is created with Meta AI at Meta, and taught by Amit Sangani, Director of Partner Engineering for Meta’s AI team. Meta’s new Llama 4 has added three new models and introduced the Mixture-of-Experts (MoE) architecture to its family of open-weight models, making them more efficient to serve. In this course, you’ll work with two of the three new models introduced in Llama 4. First is Maverick, a 400B parameter model, with 128 experts and 17B active parameters. Second is Scout, a 109B parameter model with 16 experts and 17B active parameters. Maverick and Scout support long context windows of up to a million tokens and 10M tokens, respectively. The latter is enough to support directly inputting even fairly large GitHub repos for analysis! In hands-on lessons, you’ll build apps using Llama 4’s new multimodal capabilities including reasoning across multiple images and image grounding, in which you can identify elements in images. You’ll also use the official Llama API, work with Llama 4’s long-context abilities, and learn about Llama’s newest open-source tools: its prompt optimization tool that automatically improves system prompts and synthetic data kit that generates high-quality datasets for fine-tuning. If you need an open model, Llama is a great option, and the Llama 4 family is an important part of any GenAI developer's toolkit. Through this course, you’ll learn to call Llama 4 via API, use its optimization tools, and build features that span text, images, and large context. Please sign up here:

Andrew Ng

67,587 次观看 • 1 年前

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Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:

Andrew Ng

170,240 次观看 • 1 年前

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🔗 New LangChain Academy Course: Introduction to LangChain (Python) 🔗 Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, and take action, and learn how to debug their behavior with LangSmith. Along the way, you’ll: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug your agent with LangSmith Observability & Studio By the end of the course, you’ll have assembled a full team of personal assistants. Enroll for free ➡️

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41,016 次观看 • 6 个月前

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The future of AI is open-source. And ollama is the easiest way to build AI applications with open-source LLMs. Here's how to build a free, private RAG app using open-source tools. We'll use: - Ollama for LLMs and embedding models - PostgreSQL for data storage and retrieval - pgai Vectorizer for embedding creation and sync (I use Nomic for embeddings and tinnyllama as my LLM but you can substitute them for any models on Ollama)

Avthar

34,261 次观看 • 1 年前

We studied 27 of the fastest-growing AI & crypto tools… And built a platform where you can: - Use all the best AI models (including the best open source from Hugging Face 🤗), - Earn tokens from using AI, - Co-own and trade AI models and agents, - Stake on new AI creations and get rewards every day, Want early access? 👇 Comment “AI” and we’ll send you the demo. (First 250 only – must be following)
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We studied 27 of the fastest-growing AI & crypto tools… And built a platform where you can: - Use all the best AI models (including the best open source from Hugging Face 🤗), - Earn tokens from using AI, - Co-own and trade AI models and agents, - Stake on new AI creations and get rewards every day, Want early access? 👇 Comment “AI” and we’ll send you the demo. (First 250 only – must be following)

Perspective AI

55,036 次观看 • 1 年前

New course with Hugging Face! Building Generative AI Applications with Gradio, taught by Apolinário Passos apolinário, shows you how to quickly create demos of your machine learning applications to test and iterate/share with others. Check it out!
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New course with Hugging Face! Building Generative AI Applications with Gradio, taught by Apolinário Passos apolinário, shows you how to quickly create demos of your machine learning applications to test and iterate/share with others. Check it out!

Andrew Ng

413,191 次观看 • 2 年前

Introducing OpenAI o3 and o4-mini—our smartest and most capable models to date. For the first time, our reasoning models can agentically use and combine every tool within ChatGPT, including web search, Python, image analysis, file interpretation, and image generation.
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Introducing OpenAI o3 and o4-mini—our smartest and most capable models to date. For the first time, our reasoning models can agentically use and combine every tool within ChatGPT, including web search, Python, image analysis, file interpretation, and image generation.

OpenAI

3,719,882 次观看 • 1 年前

Exciting update for AI developers! The Hugging Face Hub is now more natively integrated into Google Cloud Vertex AI Model Garden. Search through thousands of open Generative AI models from Hugging Face models & deploy them with one click to Vertex AI or GKE. 🤯 What's new: 🔎 Browse and search thousands of Hugging Face models directly within the Vertex AI Model Garden and filter based on what is currently trending in the community. 🚀 Accelerate your AI projects by leveraging readily available one-click deploy your model to Vertex AI or Google Kubernetes Engine (GKE). ⭐️ Featuring popular open models from BlackForestLabsAI - Unofficial FLUX.1, AI at Meta Llama 3.1, Mistral AI, Google DeepMind Gemma, and countless others. Get Started:
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Exciting update for AI developers! The Hugging Face Hub is now more natively integrated into Google Cloud Vertex AI Model Garden. Search through thousands of open Generative AI models from Hugging Face models & deploy them with one click to Vertex AI or GKE. 🤯 What's new: 🔎 Browse and search thousands of Hugging Face models directly within the Vertex AI Model Garden and filter based on what is currently trending in the community. 🚀 Accelerate your AI projects by leveraging readily available one-click deploy your model to Vertex AI or Google Kubernetes Engine (GKE). ⭐️ Featuring popular open models from BlackForestLabsAI - Unofficial FLUX.1, AI at Meta Llama 3.1, Mistral AI, Google DeepMind Gemma, and countless others. Get Started:

Philipp Schmid

34,754 次观看 • 1 年前

Awesome to see the collab between Kaggle and Hugging Face. Now you can use any model from Hugging Face directly in Kaggle. Previously, we had to download and upload the models as datasets. This was much needed 🚀
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Awesome to see the collab between Kaggle and Hugging Face. Now you can use any model from Hugging Face directly in Kaggle. Previously, we had to download and upload the models as datasets. This was much needed 🚀

abhishek

25,637 次观看 • 1 年前

just landed on hugging face: Step1X-Edit ✍️ & it's honestly one of the best open source image editors I've tried ✨ combines Multimodal LLM (Qwen VL) with Diffusion transformers to process and perform edit instructions ✨ apache 2.0 license ✨ new benchmark for image editing: GEdit-Bench
0:26

just landed on hugging face: Step1X-Edit ✍️ & it's honestly one of the best open source image editors I've tried ✨ combines Multimodal LLM (Qwen VL) with Diffusion transformers to process and perform edit instructions ✨ apache 2.0 license ✨ new benchmark for image editing: GEdit-Bench

Linoy Tsaban(📍🇯🇵)

56,496 次观看 • 1 年前