transformer-architecture-deepdive
CommunityDemystify Transformers: attention, position, and variants.
Software Engineering#deep learning#NLP#Transformer#neural networks#position encoding#self-attention#Vision Transformer
Authortachyon-beep
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides a deep dive into the core mechanics of Transformer architectures, explaining self-attention, position encoding, and various architectural variants. It helps you implement, debug, and optimize Transformers, ensuring you understand the "why" behind their design choices for NLP and vision tasks.
Core Features & Use Cases
- Self-Attention Mastery: Understand the information retrieval analogy, mathematical breakdown, and the role of Q, K, V matrices.
- Position Encoding Selection: Choose between sinusoidal, learned, RoPE, or ALiBi position encodings for optimal performance and extrapolation.
- Use Case: You're building a custom language model and need to decide between an encoder-only or decoder-only architecture. This skill clarifies the trade-offs, guiding you to use a decoder-only model with causal masking for text generation.
Quick Start
Explain how self-attention works and why Transformers need position encoding.
Dependency Matrix
Required Modules
torch
Components
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: transformer-architecture-deepdive Download link: https://github.com/tachyon-beep/skillpacks/archive/main.zip#transformer-architecture-deepdive Please download this .zip file, extract it, and install it in the .claude/skills/ directory.