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Embeddings (and how to create them) are, perhaps, the most interesting idea behind Large Language Models. I built a simple model to help you understand embeddings from scratch. Here is a step-by-step video explanation:

Santiago

438,377 subscribers

44,107 görüntüleme • 1 yıl önce •via X (Twitter)

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Santiago profil fotoğrafı

Santiago1 yıl önce

Here is the same video, but now on YouTube: And here is the code I used in the video:

Matt Figdore profil fotoğrafı

Matt Figdore2 yıl önce

This is the biggest productivity cheat code right now. Kiss reading documents goodbye. You can get an instant summary of any document with this tool.

Joao Marcos profil fotoğrafı

Joao Marcos1 yıl önce

@PLynchado

Sumanth profil fotoğrafı

Sumanth1 yıl önce

Impressive work Santiago. Thanks for the detailed tutorial!

Mohd Shadab Khan profil fotoğrafı

Mohd Shadab Khan1 yıl önce

How do embeddings improve AI’s reasoning? Can this method work beyond text, like for images/videos?

Santiago profil fotoğrafı

Santiago1 yıl önce

Yes, this works for images/videos. In fact, in this example, I create embeddings for images.

Foreign Policy Expert profil fotoğrafı

Foreign Policy Expert1 yıl önce

Do embeddings matter as much anymore? We’ve got smolagents, better instruct models, and bigger contexts that allow for better hierarchical search and broader contextual understanding? Embedding search is cheaper, sure, but if the docs change over time don’t the embeddings “fail”

Ray | AI marketer - Social Media Assistant profil fotoğrafı

Ray | AI marketer - Social Media Assistant1 yıl önce

How do you see embeddings improving the efficiency of your model? They can really help in capturing semantic relationships, which is crucial for better understanding context in AI.

Uncle Drei profil fotoğrafı

Uncle Drei1 yıl önce

@memdotai meme this

scuzzlebot profil fotoğrafı

scuzzlebot1 yıl önce

Great educational content on embeddings! These fundamental concepts are crucial for understanding how modern AI systems work. Looking forward to the video explanation.

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The most clearest and crisp explanation, I've ever heard, of how large language models compress and capture a "world-model" in their weights simply by learning to predict the next word accurately. Furthermore, how the raw power of these base models can then be tamed by teaching them to follow instructions from humans. Source:

The most clearest and crisp explanation, I've ever heard, of how large language models compress and capture a "world-model" in their weights simply by learning to predict the next word accurately. Furthermore, how the raw power of these base models can then be tamed by teaching them to follow instructions from humans. Source:

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SceneScript treats 3D reconstruction as a language problem rather than a geometry one. The model watches a video of a room and just learns to write a script for it. It autoregressively spits out text commands like make_wall(...) or make_bbox(...) that define the scene. Stanford's new "Scene Language" paper goes a step further adding CLIP embeddings to capture visual appearance too. The fact that language models already understand spatial relationships well enough to write out scene graphs is pretty wild.

SceneScript treats 3D reconstruction as a language problem rather than a geometry one. The model watches a video of a room and just learns to write a script for it. It autoregressively spits out text commands like make_wall(...) or make_bbox(...) that define the scene. Stanford's new "Scene Language" paper goes a step further adding CLIP embeddings to capture visual appearance too. The fact that language models already understand spatial relationships well enough to write out scene graphs is pretty wild.

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[1/n] Do distinct large models admit a simple map that aligns their embedding spaces? We show that across multimodal contrastive models—trained on different data and architectures—an orthogonal map aligns image embeddings. Strikingly, the same map also aligns text embeddings.

[1/n] Do distinct large models admit a simple map that aligns their embedding spaces? We show that across multimodal contrastive models—trained on different data and architectures—an orthogonal map aligns image embeddings. Strikingly, the same map also aligns text embeddings.

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building large language models from scratch by Sebastian Raschka was a great chance for me to sit down and study again all the LLM basics > token and positional embeddings > self-attention and what QKV is about > causal & multi-head attention studying llms and how they work can seem overwhelming at first. but once you taste how good it feels to learn these things intuitively there's no going back. I shared the resources and my notes on my repo and I hope it's a motivation if you want to start as well or recap.

building large language models from scratch by Sebastian Raschka was a great chance for me to sit down and study again all the LLM basics > token and positional embeddings > self-attention and what QKV is about > causal & multi-head attention studying llms and how they work can seem overwhelming at first. but once you taste how good it feels to learn these things intuitively there's no going back. I shared the resources and my notes on my repo and I hope it's a motivation if you want to start as well or recap.

ℏεsam

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Why Ticks are very difficult to remove...but a simple step from the video will help you easily 🤗

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We just released "Large Language Models with Semantic Search”, built with Cohere, and taught by Jay Alammar and Luis Serrano. Search is a key part of many applications. Say, you need to retrieve documents or products in response to a user query; how can LLMs help? You’ll learn about (i) Embeddings, to retrieve a collection of documents loosely related to a query, and (ii) LLM assisted re-ranking, to rank them precisely according to relevance. You’ll also go through code showing how to tie all this together to build a complete search system for retrieving relevant Wikipedia articles. Please check it out!

We just released "Large Language Models with Semantic Search”, built with Cohere, and taught by Jay Alammar and Luis Serrano. Search is a key part of many applications. Say, you need to retrieve documents or products in response to a user query; how can LLMs help? You’ll learn about (i) Embeddings, to retrieve a collection of documents loosely related to a query, and (ii) LLM assisted re-ranking, to rank them precisely according to relevance. You’ll also go through code showing how to tie all this together to build a complete search system for retrieving relevant Wikipedia articles. Please check it out!

Andrew Ng

595,971 görüntüleme • 2 yıl önce

Here is a killer way to use embeddings: I built a notebook to show you how to do anomaly detection on images. Anomaly detection is one of the most common problems in the real world. Learn how to implement anomaly detection, and you'll be able to solve a ton of use cases that happen every day: • Predicting equipment failures • Identifying defective products • Identifying tumors in medical images • Spotting counterfeit products • Detecting fraud • Flagging fake reviews • Detecting bot traffic My notebook focuses on images. It uses: • Langchain • OpenClip embeddings • Oracle vector store This is the way my example works: • You start with a collection of similar images • You can then check whether a new image "belongs" to that collection I generate embeddings and use Oracle 26ai to store them and retrieve similar images on demand.

Here is a killer way to use embeddings: I built a notebook to show you how to do anomaly detection on images. Anomaly detection is one of the most common problems in the real world. Learn how to implement anomaly detection, and you'll be able to solve a ton of use cases that happen every day: • Predicting equipment failures • Identifying defective products • Identifying tumors in medical images • Spotting counterfeit products • Detecting fraud • Flagging fake reviews • Detecting bot traffic My notebook focuses on images. It uses: • Langchain • OpenClip embeddings • Oracle vector store This is the way my example works: • You start with a collection of similar images • You can then check whether a new image "belongs" to that collection I generate embeddings and use Oracle 26ai to store them and retrieve similar images on demand.

Santiago

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Introducing WildDet3D, a grounding model for monocular 3D object detection in the wild. A question I keep coming back to is: what is the right backbone for robotics foundation models? Should it be a video model, a language model, or perhaps a grounding model? WildDet3D is our first step in exploring that direction.

Introducing WildDet3D, a grounding model for monocular 3D object detection in the wild. A question I keep coming back to is: what is the right backbone for robotics foundation models? Should it be a video model, a language model, or perhaps a grounding model? WildDet3D is our first step in exploring that direction.

Jiafei Duan

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This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the Timescale (now TigerData) team for partnering with me on this post!

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the Timescale (now TigerData) team for partnering with me on this post!

Santiago

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lincoln.base.eth

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Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

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