Does it provide support for thinking models like Qwen3?

Yes, it includes specific patterns for parsing thinking blocks using special tokens and formatting chat templates for reasoning-based models.

Are the code examples compatible with modern deep learning frameworks?

The implementation examples are provided in PyTorch, making them compatible with the most widely used libraries for model development and fine-tuning.

What components of the Transformer architecture are covered?

This skill covers self-attention, multi-head attention, feed-forward networks (FFN), layer normalization, and residual connections.

How does this skill assist with fine-tuning?

It helps developers identify which layers and modules (like Q, K, V projections) should be targeted for Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA.

Can this skill help me estimate how much memory an LLM will use?

Yes, it includes model size estimation logic that calculates billions of parameters based on vocabulary size, embedding dimensions, and layer counts.

Transformer Architecture Guide

Name: Transformer Architecture Guide
Author: atrawog

byatrawog

Ciencia de Datos y ML

Explains and implements core Transformer architecture components for LLM development, fine-tuning, and model analysis.

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This skill provides deep technical guidance on the Transformer architecture, the foundation of modern large language models. It covers essential mechanisms like self-attention, multi-head attention, and feed-forward networks with practical PyTorch implementations. It is particularly useful for developers and data scientists who need to understand model internals for making fine-tuning decisions, debugging attention patterns, selecting LoRA targets, or estimating model parameters and memory requirements for deployment. Additionally, it includes specialized support for handling 'thinking' model tokens and parsing chain-of-thought outputs for advanced AI reasoning workflows.

Características Principales

Guidance on identifying target modules for PEFT and LoRA fine-tuning
Comprehensive walkthroughs of self-attention and multi-head attention mechanisms
0 GitHub stars
Model parameter and size estimation utilities for capacity planning
Functional PyTorch implementation examples for all Transformer components
Specialized token handling for Qwen3-style thinking and reasoning models

Casos de Uso

Integrating chain-of-thought reasoning tokens into production LLM pipelines
Designing and debugging custom Transformer layers for specialized AI models
Optimizing fine-tuning strategies by identifying relevant model components

Acerca de

Características Principales

Guidance on identifying target modules for PEFT and LoRA fine-tuning
Comprehensive walkthroughs of self-attention and multi-head attention mechanisms
0 GitHub stars
Model parameter and size estimation utilities for capacity planning
Functional PyTorch implementation examples for all Transformer components
Specialized token handling for Qwen3-style thinking and reasoning models

Casos de Uso

Integrating chain-of-thought reasoning tokens into production LLM pipelines
Designing and debugging custom Transformer layers for specialized AI models
Optimizing fine-tuning strategies by identifying relevant model components