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THE BEST visual explainer of how information propagates through a transformer. If you want to have more than intuition about how the Transformer architecture is ruling the LLM world - open-source project explains everything about LLM Transformer Models! - A great resource for anyone looking to gain a deeper...

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Rohan Paul

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106,897 görüntüleme • 1 yıl önce •via X (Twitter)

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Wow. This is one of the best interactive sites I’ve seen for learning how LLMs work! 🔥 It starts w/ a clear intro and guides you through every core component: from Embedding, Layer Norm, and Self-Attention to MLPs, Transformer blocks, Softmax, and Output layers. link in 🧵↓
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Wow. This is one of the best interactive sites I’ve seen for learning how LLMs work! 🔥 It starts w/ a clear intro and guides you through every core component: from Embedding, Layer Norm, and Self-Attention to MLPs, Transformer blocks, Softmax, and Output layers. link in 🧵↓

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Transformer by hand✍️ Excel ~ I designed this exercise to show the core math of a Transformer model is to combine columns (attention), combine rows (feed forward), and repeat.👇Join the 'AI Math' community. 👇Download xlsx.
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Transformer by hand✍️ Excel ~ I designed this exercise to show the core math of a Transformer model is to combine columns (attention), combine rows (feed forward), and repeat.👇Join the 'AI Math' community. 👇Download xlsx.

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Can we make Transformers better and more efficient for robot learning? Excited to introduce Body Transformer (BoT), an architecture that leverages robot embodiment in the attention mechanism, by treating it as a graph of sensors and actuators.
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Can we make Transformers better and more efficient for robot learning? Excited to introduce Body Transformer (BoT), an architecture that leverages robot embodiment in the attention mechanism, by treating it as a graph of sensors and actuators.

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Transformer: Multi-Head Attention ~ Math vs Code 🔢💻 ~ I made this visualization to show you how to implement the multi-head attention math in PyTorch within 50 LoC. Multi-Head Attention is what makes the Transformer's performance outstanding. It captures and represents more diverse linguistic relationships and patterns, and attends to different learned input embedding spaces. The parallel computing design also makes the model more efficient.
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Transformer: Multi-Head Attention ~ Math vs Code 🔢💻 ~ I made this visualization to show you how to implement the multi-head attention math in PyTorch within 50 LoC. Multi-Head Attention is what makes the Transformer's performance outstanding. It captures and represents more diverse linguistic relationships and patterns, and attends to different learned input embedding spaces. The parallel computing design also makes the model more efficient.

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How do you like my transformer?
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How do you like my transformer?

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The rise and fall of popular transformer architectures 📈 One pro of having a lot of transformer architectures implemented in transformers & hosted on the Hugging Face Hub is that one can see the evolution of popularity of different models 👀
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The rise and fall of popular transformer architectures 📈 One pro of having a lot of transformer architectures implemented in transformers & hosted on the Hugging Face Hub is that one can see the evolution of popularity of different models 👀

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🚨 This is the best way to learn how LLMs work. Interactive. 3D. Step-by-step. Covers: → Embedding → Layer Norm → Self-Attention → MLP → Transformer layers → Softmax → Output Stop reading papers. Start seeing. Link in comments. Save this immediately.
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🚨 This is the best way to learn how LLMs work. Interactive. 3D. Step-by-step. Covers: → Embedding → Layer Norm → Self-Attention → MLP → Transformer layers → Softmax → Output Stop reading papers. Start seeing. Link in comments. Save this immediately.

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We’re releasing model weights for our 8B- parameter Dynamic Byte Latent Transformer, an alternative to traditional tokenization methods with the potential to redefine the standards for language model efficiency and reliability. Learn more about how Dynamic Byte Latent Transformer is paving the way for groundbreaking developments in the field of language modeling. Read the research paper, and download the model and code.
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We’re releasing model weights for our 8B- parameter Dynamic Byte Latent Transformer, an alternative to traditional tokenization methods with the potential to redefine the standards for language model efficiency and reliability. Learn more about how Dynamic Byte Latent Transformer is paving the way for groundbreaking developments in the field of language modeling. Read the research paper, and download the model and code.

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Check this!! A 100% open-source toolkit to work with LLMs. Transformer Lab is an app to experiment with LLMs: - Train, fine-tune, or chat. - One-click LLM download (DeepSeek, Gemma, etc.) - Drag-n-drop UI for RAG. - Built-in logging, and more. 100% local!
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Check this!! A 100% open-source toolkit to work with LLMs. Transformer Lab is an app to experiment with LLMs: - Train, fine-tune, or chat. - One-click LLM download (DeepSeek, Gemma, etc.) - Drag-n-drop UI for RAG. - Built-in logging, and more. 100% local!

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Part 2/30 of the LLM Series: RoPE (Rotary Position Embedding) How does a transformer know the difference between - "the dog bit the man" and "the man bit the dog"? The words are almost identical, but the meaning changes completely. RoPE encodes position as rotation, allowing transformers to understand relative order through geometry. Read more:
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Part 2/30 of the LLM Series: RoPE (Rotary Position Embedding) How does a transformer know the difference between - "the dog bit the man" and "the man bit the dog"? The words are almost identical, but the meaning changes completely. RoPE encodes position as rotation, allowing transformers to understand relative order through geometry. Read more:

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Get ready for the PDE-Transformer: our new NN architecture tailored to scientific tasks. It combines hierarchical processing (UDiT), scalability (SWin) and flexible conditioning mechanisms. The paper shows it outperforming existing SOTA architectures 😁
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Get ready for the PDE-Transformer: our new NN architecture tailored to scientific tasks. It combines hierarchical processing (UDiT), scalability (SWin) and flexible conditioning mechanisms. The paper shows it outperforming existing SOTA architectures 😁

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Transformers & LLMs cheatsheets for Stanford's CME-295! Covering tokenization, self-attention, prompting, fine-tuning, LLM-as-a-judge, RAG, AI Agents, and reasoning models. 100% free and open-source.
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Transformers & LLMs cheatsheets for Stanford's CME-295! Covering tokenization, self-attention, prompting, fine-tuning, LLM-as-a-judge, RAG, AI Agents, and reasoning models. 100% free and open-source.

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The best Tesla costume. Transformer.

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Switti -- a new scale-wise transformer for text-to-image generation 🦾 🔥 Improved generation of fine-grained details. Outperforms existing T2I AR models and competes with state-of-the-art T2I diffusion models while being up to 7x faster.
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Switti -- a new scale-wise transformer for text-to-image generation 🦾 🔥 Improved generation of fine-grained details. Outperforms existing T2I AR models and competes with state-of-the-art T2I diffusion models while being up to 7x faster.

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THIS HAPPENED YESTERDAY IN ENUGU! Transformer vandalizer electrocuted while trying to steal from the transformer. 💔😭
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THIS HAPPENED YESTERDAY IN ENUGU! Transformer vandalizer electrocuted while trying to steal from the transformer. 💔😭

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Show-o One Single Transformer to Unify Multimodal Understanding and Generation discuss: We present a unified transformer, i.e., Show-o, that unifies multimodal understanding and generation. Unlike fully autoregressive models, Show-o unifies autoregressive and (discrete) diffusion modeling to adaptively handle inputs and outputs of various and mixed modalities. The unified model flexibly supports a wide range of vision-language tasks including visual question-answering, text-to-image generation, text-guided inpainting/extrapolation, and mixed-modality generation. Across various benchmarks, it demonstrates comparable or superior performance to existing individual models with an equivalent or larger number of parameters tailored for understanding or generation. This significantly highlights its potential as a next-generation foundation model.

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