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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 Aufrufe • vor 1 Jahr •via X (Twitter)

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10 Kommentare

Profilbild von Santiago

Santiagovor 1 Jahr

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

Profilbild von Matt Figdore

Matt Figdorevor 2 Jahren

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.

Profilbild von Joao Marcos

Joao Marcosvor 1 Jahr

@PLynchado

Profilbild von Sumanth

Sumanthvor 1 Jahr

Impressive work Santiago. Thanks for the detailed tutorial!

Profilbild von Mohd Shadab Khan

Mohd Shadab Khanvor 1 Jahr

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

Profilbild von Santiago

Santiagovor 1 Jahr

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

Profilbild von Foreign Policy Expert

Foreign Policy Expertvor 1 Jahr

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”

Profilbild von Ray | AI marketer - Social Media Assistant

Ray | AI marketer - Social Media Assistantvor 1 Jahr

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.

Profilbild von Uncle Drei

Uncle Dreivor 1 Jahr

@memdotai meme this

Profilbild von scuzzlebot

scuzzlebotvor 1 Jahr

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:

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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.

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