Understanding Transformers (beyond the Math) – kalomaze's kalomazing blog
The blog post explains transformer architecture in language models, highlighting their role as state simulators, the significance of output distributions, and the impact of temperature settings on predictions and adaptability.
Read original article
The blog post discusses an informal approach to understanding transformer architecture in language models, emphasizing the importance of grasping the broader system rather than just the technical details. The author suggests that transformers function as state simulators, where each prediction has its own state that can change based on new information, rather than following a linear progression. This allows for in-context learning and spontaneity. The output layer is explained as generating distributions of possible next tokens rather than simply predicting the most likely one. The author clarifies misconceptions about temperature settings in token prediction, explaining that lower temperatures sharpen the distribution, leading to potential repetition in outputs. The post also touches on an ASCII art diffusion experiment, illustrating the model's ability to adapt and generate structured outputs from random inputs. Overall, the piece advocates for a more intuitive understanding of transformers, focusing on their capabilities and the underlying principles that govern their operation.
- Transformers act as state simulators, allowing for dynamic predictions based on context.
- The output layer generates distributions of tokens, not just the most likely next token.
- Temperature settings influence the sharpness of token distributions, affecting output variability.
- Understanding transformers requires a focus on their broader functionality rather than just technical details.
- The author shares an ASCII art experiment to demonstrate the model's adaptability and learning process.
Related
The Illustrated Transformer
Jay Alammar's blog explores The Transformer model, highlighting its attention mechanism for faster training. It outperforms Google's NMT in some tasks, emphasizing parallelizability. The blog simplifies components like self-attention and multi-headed attention for better understanding.
Math Behind Transformers and LLMs
This post introduces transformers and large language models, focusing on OpenGPT-X and transformer architecture. It explains language models, training processes, computational demands, GPU usage, and the superiority of transformers in NLP.
Transformer Layers as Painters
The study "Transformer Layers as Painters" by Qi Sun et al. delves into transformer models, showcasing layer impact variations and potential for model optimization through strategic layer adjustments.
Transformer Explainer: An Interactive Explainer of the Transformer Architecture
The Transformer architecture has transformed AI in text generation, utilizing self-attention and advanced features like layer normalization. The Transformer Explainer tool helps users understand its concepts interactively.
Transformer Explainer
The Transformer architecture has transformed AI in text generation, utilizing self-attention and key components like embedding and Transformer blocks, while advanced features enhance performance and stability.
1 comments>they're completely stateless in their architecture
Wait, what? The basic fact it uses most recently generated tokens to predict the next one (or rather the distribution, not the point) seems to contradict that.
Related
The Illustrated Transformer
Jay Alammar's blog explores The Transformer model, highlighting its attention mechanism for faster training. It outperforms Google's NMT in some tasks, emphasizing parallelizability. The blog simplifies components like self-attention and multi-headed attention for better understanding.
Math Behind Transformers and LLMs
This post introduces transformers and large language models, focusing on OpenGPT-X and transformer architecture. It explains language models, training processes, computational demands, GPU usage, and the superiority of transformers in NLP.
Transformer Layers as Painters
The study "Transformer Layers as Painters" by Qi Sun et al. delves into transformer models, showcasing layer impact variations and potential for model optimization through strategic layer adjustments.
Transformer Explainer: An Interactive Explainer of the Transformer Architecture
The Transformer architecture has transformed AI in text generation, utilizing self-attention and advanced features like layer normalization. The Transformer Explainer tool helps users understand its concepts interactively.
Transformer Explainer
The Transformer architecture has transformed AI in text generation, utilizing self-attention and key components like embedding and Transformer blocks, while advanced features enhance performance and stability.