transformer-architecture-guide
Guide to Transformer architectures for NLP and computer vision
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill transformer-architecture-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Transformer Architecture Guide Understand, implement, and adapt Transformer architectures for NLP, computer vision, and multimodal research, from the original attention mechanism to modern variants. ## The Original Transformer The Transformer (Vaswani et al., 2017, "Attention Is All You Need") replaced recurrence and convolution with self-attention as the primary sequence modeling mechanism. ### Core Components | Component | Function | Key Parameters | |-----------|----------|---------------| | Multi-Head Self-Attention | Computes attention weights across all positions | d_model, n_heads, d_k, d_v | | Feed-Forward Network | Position-wise nonlinear transformation | d_model, d_ff | | Positional Encoding | Injects sequence order information | Sinusoidal or learned | | Layer Normalization | Stabilizes training | Pre-norm or post-norm | | Residual Connections | Enables gradient flow in deep networks | Add before or after norm | ### Self-Attention Mechanism ```python import torch import torch.nn as nn import torch.nn.functional as F import math class MultiHeadAttention(nn.Module): def __init__(self, d_model=512, n_heads=8): super().__init__() self.d_model = d_model self.n_heads = n_hea
- The Original Transformer
- Core Components
- Self-Attention Mechanism
- Complete Transformer Block
- Major Transformer Variants
- Architecture Taxonomy
- Encoder-Only (BERT Family)
- Decoder-Only (GPT Family)
- Vision Transformers (ViT)
- Efficient Transformer Variants
- Model Scaling Laws
- Research Resources
What does the transformer-architecture-guide skill do?
Guide to Transformer architectures for NLP and computer vision
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill transformer-architecture-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.