Architectures, ready to open
36 architectures kept as typed graphs, not pictures: 2034 layers in total, every tensor shape propagated, 25 of them clean under all 41 structural checks. Open one on the canvas and it is yours to edit, verify and export.
Audio
Biosignal
Computer Vision
Generative
Multimodal
NLP
NLP/LLM
Recommendation
Time-series
index.json
Audio
Biosignal
🧠 EEGNet
Compact CNN for EEG/BCI — depthwise + separable convs make it 10× lighter than standard CNNs
🧠 EEG Conformer
Conv stem + Transformer encoder — SOTA for high-channel motor imagery EEG
Computer Vision
🖼️ Simple CNN
Simple Convolutional Neural Network for image classification
🔗 ResNet Block
ResNet residual block with skip connections
🩻 U-Net
Encoder-decoder with skip connections — Ronneberger et al
👁️ ViT-B/16
Vision Transformer — patch embedding stem + 1 encoder block
🪟 Swin-Tiny
Hierarchical vision transformer — shifted-window attention builds a feature pyramid for dense prediction
Generative
🎨 Diffusion UNet
Stable-Diffusion-style noise predictor — latent UNet with cross-attention to a text embedding
🌀 DiT-XL/2
Diffusion Transformer — replaces the UNet denoiser with a ViT backbone conditioned on timestep + class via adaLN-Zero
Multimodal
🔗 CLIP ViT-B/32
Dual-encoder contrastive model — a ViT image tower and a Transformer text tower projected into a shared embedding space
👁️ LLaVA-1.5
Vision-language model — CLIP image encoder + MLP projector feed visual tokens into a LLaMA decoder
NLP
NLP/LLM
🤖 Transformer Block
Transformer encoder block
📖 BERT Base
BERT-Base encoder — bidirectional MHA
🧠 GPT-2
GPT-2 Small — causal transformer block
🦙 LLaMA-3 Block
LLaMA-3 decoder block — GQA
🔀 Mixtral MoE Block
Mixtral decoder block — GQA + Sparse MoE
🔁 T5 Small
T5 encoder-decoder — bidirectional encoder + masked decoder with cross-attention
🐍 Mamba SSM Block
Mamba State Space Model — selective SSM + causal conv gating, no attention
φ Phi-3 Mini Block
Phi-3 Mini 3
🐋 DeepSeek-V3
671B MoE LLM — Multi-head Latent Attention
🦙 Llama-4 Scout
109B natively-multimodal MoE LLM — interleaved dense/MoE layers
🧬 Jamba
Hybrid SSM-Transformer-MoE — interleaves Mamba, attention, and MoE blocks in one stack
🟣 Qwen3-8B
Modern dense decoder LLM — GQA with QK-RMSNorm on the query/key projections for training stability
Recommendation
🗼 Two-Tower
User+Item dual encoder for retrieval — embeddings → MLP per side → dot product score
📐 Wide & Deep
Memorization
🛒 DLRM
Meta's Deep Learning Recommendation Model — bottom MLP for dense, embedding for sparse, feature interaction, top MLP
🤝 Neural Collaborative Filtering
He et al
🕸 GraphSAGE Recommender
Inductive node embeddings via neighbor sampling + aggregation — for graph-based recommenders
🧮 Neural Collaborative Filtering
He et al
💡 LightGCN
He et al
🧾 Behavior Sequence Transformer
Alibaba 2019 BST — user behavior sequence + target → Transformer encoder + concat with user features → MLP → CTR
🕒 SLi-Rec
Yu et al
Time-series
📈 PatchTST
Channel-independent patching + Transformer for multivariate time-series
📈 1D CNN + LSTM
Conv1D + LSTM baseline for ECG/PPG/IMU and other long-form physio signals
For agents
Every architecture here is a graph an agent can fetch and edit. index.json lists all 36 with the graph URL, the parameter count and the verifier's verdict; each has a markdown twin. Send an edited graph to POST /api/v1/check and the same checks grade it, or run the MCP server to keep them in the loop while the agent works.