Datasets:
key stringlengths 3 17 | name stringlengths 4 30 | category stringclasses 9
values | description stringlengths 38 145 | whenToUse stringlengths 116 229 | layers int64 4 434 | params int64 2.72k 666B | paramsHuman stringlengths 4 7 | inputShape listlengths 1 3 | outputShape listlengths 1 3 | verdict stringclasses 2
values | findings listlengths 0 8 | page stringlengths 31 45 | markdown stringlengths 29 43 | graph stringlengths 45 59 | openInApp stringlengths 34 48 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
eegnet | EEGNet | Biosignal | Compact CNN for EEG/BCI — depthwise + separable convs make it 10× lighter than standard CNNs (Lawhern 2018) | Pick when channel count is low (4–8) and labelled trials are scarce (<400). Strong default for motor imagery / P300 BCI on consumer headsets. | 16 | 2,724 | 2.7K | [
1,
22,
1000
] | [
4
] | pass | [] | https://neurarch.com/a/eegnet.html | https://neurarch.com/a/eegnet.md | https://neurarch.com/templates/eegnet/model.json | https://neurarch.com/?template=eegnet |
eeg-conformer | EEG Conformer | Biosignal | Conv stem + Transformer encoder — SOTA for high-channel motor imagery EEG (Song 2023) | Pick when you have ≥16 channels and ~400+ trials per subject. Best published accuracy on BCI IV-2a/2b; expect tricky regularization. | 23 | 78,564 | 78.6K | [
1,
22,
1000
] | [
4
] | warn | [
{
"rule": "attention-no-pe",
"severity": "warn"
}
] | https://neurarch.com/a/eeg-conformer.html | https://neurarch.com/a/eeg-conformer.md | https://neurarch.com/templates/eeg-conformer/model.json | https://neurarch.com/?template=eeg-conformer |
patch-tst | PatchTST | Time-series | Channel-independent patching + Transformer for multivariate time-series (Nie 2023) | Pick for multivariate time-series with long windows where channels can be treated independently. Same backbone serves classification and forecasting. | 19 | 395,396 | 395.4K | [
1,
22,
1000
] | [
4
] | pass | [] | https://neurarch.com/a/patch-tst.html | https://neurarch.com/a/patch-tst.md | https://neurarch.com/templates/patch-tst/model.json | https://neurarch.com/?template=patch-tst |
cnn-lstm-1d | 1D CNN + LSTM | Time-series | Conv1D + LSTM baseline for ECG/PPG/IMU and other long-form physio signals | Pick for long-form 1D physiological signals (ECG, PPG, IMU) where local morphology + temporal context both matter. Solid baseline before reaching for transformers. | 14 | 311,749 | 311.7K | [
12,
5000
] | [
5
] | pass | [] | https://neurarch.com/a/cnn-lstm-1d.html | https://neurarch.com/a/cnn-lstm-1d.md | https://neurarch.com/templates/cnn-lstm-1d/model.json | https://neurarch.com/?template=cnn-lstm-1d |
simple-cnn | Simple CNN | Computer Vision | Simple Convolutional Neural Network for image classification | Pick as a fast baseline for small images (≤64px, e.g. CIFAR). Trains in minutes, easy to debug — use before reaching for ResNet. | 9 | 804,554 | 804.6K | [
1,
28,
28
] | [
10
] | pass | [
{
"rule": "deep-no-norm",
"severity": "info"
}
] | https://neurarch.com/a/simple-cnn.html | https://neurarch.com/a/simple-cnn.md | https://neurarch.com/templates/simple-cnn/model.json | https://neurarch.com/?template=simple-cnn |
resnet-block | ResNet Block | Computer Vision | ResNet residual block with skip connections | Pick when you need a depth-friendly CV backbone. Stack 2–4 blocks for CIFAR, or use as the building block of ResNet-18/50 for ImageNet-scale. | 9 | 73,984 | 74.0K | [
64,
32,
32
] | [
64,
32,
32
] | pass | [] | https://neurarch.com/a/resnet-block.html | https://neurarch.com/a/resnet-block.md | https://neurarch.com/templates/resnet-block/model.json | https://neurarch.com/?template=resnet-block |
unet | U-Net | Computer Vision | Encoder-decoder with skip connections — Ronneberger et al. 2015. The standard for biomedical and small-data image segmentation. | Pick for image segmentation when training data is limited (<10k images). Skip connections preserve fine spatial detail that pure encoder-decoders lose. Default for medical imaging, satellite, and any pixel-level binary mask task. | 24 | 720,705 | 720.7K | [
3,
256,
256
] | [
1,
256,
768
] | warn | [
{
"rule": "deep-no-residual",
"severity": "warn"
}
] | https://neurarch.com/a/unet.html | https://neurarch.com/a/unet.md | https://neurarch.com/templates/unet/model.json | https://neurarch.com/?template=unet |
vit-b16 | ViT-B/16 | Computer Vision | Vision Transformer — patch embedding stem + 1 encoder block (768D, 12 heads) | Pick for 224px+ images when pretrained weights are available, or when dataset is large enough (>1M images) to train from scratch. | 13 | 8,449,000 | 8.45M | [
3,
224,
224
] | [
196,
1000
] | pass | [
{
"rule": "dropout-before-bn",
"severity": "info"
}
] | https://neurarch.com/a/vit-b16.html | https://neurarch.com/a/vit-b16.md | https://neurarch.com/templates/vit-b16/model.json | https://neurarch.com/?template=vit-b16 |
transformer-block | Transformer Block | NLP/LLM | Transformer encoder block (simplified) | Pick as a generic encoder building block for sequence models when you don't need the modern LLM stack (RoPE/GQA/RMSNorm). Good teaching baseline. | 8 | 7,087,872 | 7.09M | [
512,
768
] | [
512,
768
] | warn | [
{
"rule": "bn-at-output",
"severity": "warn"
},
{
"rule": "attention-no-pe",
"severity": "warn"
}
] | https://neurarch.com/a/transformer-block.html | https://neurarch.com/a/transformer-block.md | https://neurarch.com/templates/transformer-block/model.json | https://neurarch.com/?template=transformer-block |
bert-base | BERT Base | NLP/LLM | BERT-Base encoder — bidirectional MHA (12 heads, 768D), no causal mask, 30K vocab | Pick for text classification / NLU with limited labels — pretrained encoder + small head fine-tunes reliably. 512-token cap. | 11 | 31,120,896 | 31.12M | [
1,
512
] | [
1,
512,
768
] | pass | [] | https://neurarch.com/a/bert-base.html | https://neurarch.com/a/bert-base.md | https://neurarch.com/templates/bert-base/model.json | https://neurarch.com/?template=bert-base |
gpt2 | GPT-2 | NLP/LLM | GPT-2 Small — causal transformer block (768D, 12 heads, 4× FFN) | Pick for single-GPU language modeling experiments and as a teaching reference for the canonical decoder-only stack. Modern LLMs prefer LLaMA-3 / Phi-3 blocks. | 12 | 84,331,345 | 84.33M | [
1,
1024
] | [
1,
1024,
50257
] | pass | [] | https://neurarch.com/a/gpt2.html | https://neurarch.com/a/gpt2.md | https://neurarch.com/templates/gpt2/model.json | https://neurarch.com/?template=gpt2 |
llama3-block | LLaMA-3 Block | NLP/LLM | LLaMA-3 decoder block — GQA (32H/8KV), SwiGLU FFN, RMSNorm, residual streams | Pick when building a modern dense LLM (7B–70B class). Strongest open recipe per param; GQA cuts KV cache, RoPE handles long context. | 10 | 702,554,112 | 702.55M | [
1,
2048
] | [
1,
2048,
4096
] | pass | [] | https://neurarch.com/a/llama3-block.html | https://neurarch.com/a/llama3-block.md | https://neurarch.com/templates/llama3-block/model.json | https://neurarch.com/?template=llama3-block |
mixtral-block | Mixtral MoE Block | NLP/LLM | Mixtral decoder block — GQA + Sparse MoE (8 experts, top-2) + RMSNorm + RoPE | Pick when you have MoE training infra and want best quality per active param. Routing instability and memory cost (still scales with total params) are the trade-off. | 9 | 1,451,270,144 | 1.45B | [
1,
4096,
4096
] | [
1,
4096,
4096
] | pass | [
{
"rule": "moe-no-aux-loss",
"severity": "info"
}
] | https://neurarch.com/a/mixtral-block.html | https://neurarch.com/a/mixtral-block.md | https://neurarch.com/templates/mixtral-block/model.json | https://neurarch.com/?template=mixtral-block |
t5-small | T5 Small | NLP/LLM | T5 encoder-decoder — bidirectional encoder + masked decoder with cross-attention (512D, 8 heads) | Pick for seq2seq tasks (summarization, translation, QA) where you need both bidirectional understanding and generation in one model. | 23 | 56,734,592 | 56.73M | [
1,
512
] | [
1,
128,
32128
] | warn | [
{
"rule": "attention-no-pe",
"severity": "warn"
}
] | https://neurarch.com/a/t5-small.html | https://neurarch.com/a/t5-small.md | https://neurarch.com/templates/t5-small/model.json | https://neurarch.com/?template=t5-small |
whisper-small | Whisper Small | Audio | Whisper speech encoder-decoder — conv1d audio stem + transformer encoder/decoder (384D) | Pick for ASR or as a pretrained audio encoder — drop the decoder + add a head for audio classification (UrbanSound, ESC-50). | 18 | 46,986,649 | 46.99M | [
1,
80,
3000
] | [
1,
448,
51865
] | pass | [] | https://neurarch.com/a/whisper-small.html | https://neurarch.com/a/whisper-small.md | https://neurarch.com/templates/whisper-small/model.json | https://neurarch.com/?template=whisper-small |
simple-rnn | Simple RNN | NLP | Simple Recurrent Neural Network for sequence processing | Pick as a teaching reference. Real workloads should reach for LSTM/GRU or transformer — vanilla RNN suffers from vanishing gradients on anything beyond ~50 steps. | 4 | 1,100,298 | 1.10M | [
128,
300
] | [
128,
10
] | pass | [] | https://neurarch.com/a/simple-rnn.html | https://neurarch.com/a/simple-rnn.md | https://neurarch.com/templates/simple-rnn/model.json | https://neurarch.com/?template=simple-rnn |
mamba-block | Mamba SSM Block | NLP/LLM | Mamba State Space Model — selective SSM + causal conv gating, no attention (O(T) complexity) | Pick for very long sequences where attention's O(T²) cost is the bottleneck (DNA, audio, long-context LM). Tuning is trickier than transformers. | 18 | 168,271,976 | 168.27M | [
1,
1024
] | [
1,
1024,
50280
] | pass | [] | https://neurarch.com/a/mamba-block.html | https://neurarch.com/a/mamba-block.md | https://neurarch.com/templates/mamba-block/model.json | https://neurarch.com/?template=mamba-block |
phi3-mini | Phi-3 Mini Block | NLP/LLM | Phi-3 Mini 3.8B decoder block — full MHA (32H, 3072D), SwiGLU FFN (8192), RMSNorm, RoPE | Pick for compact LLMs (≤4B params) when you want modern ingredients (RoPE, SwiGLU) without the full LLaMA-3 footprint. | 12 | 310,288,704 | 310.29M | [
1,
2048
] | [
1,
2048,
32064
] | pass | [] | https://neurarch.com/a/phi3-mini.html | https://neurarch.com/a/phi3-mini.md | https://neurarch.com/templates/phi3-mini/model.json | https://neurarch.com/?template=phi3-mini |
two-tower | Two-Tower | Recommendation | User+Item dual encoder for retrieval — embeddings → MLP per side → dot product score | Pick for retrieval at scale (billions of items) where item embeddings can be precomputed and indexed. Not suitable for re-ranking — no cross-features. | 12 | 70,433,152 | 70.43M | [
1
] | [
1,
64
] | pass | [
{
"rule": "deep-no-norm",
"severity": "info"
}
] | https://neurarch.com/a/two-tower.html | https://neurarch.com/a/two-tower.md | https://neurarch.com/templates/two-tower/model.json | https://neurarch.com/?template=two-tower |
wide-and-deep | Wide & Deep | Recommendation | Memorization (wide linear) + generalization (deep MLP) joint trained — Cheng et al. 2016 | Pick as a simpler, more interpretable alternative to DLRM for CTR. Linear part captures memorized rules, deep part generalizes. | 13 | 3,652,882 | 3.65M | [
10000
] | [
1
] | pass | [
{
"rule": "output-activation",
"severity": "info"
},
{
"rule": "vanishing-gradient",
"severity": "info"
},
{
"rule": "deep-no-norm",
"severity": "info"
}
] | https://neurarch.com/a/wide-and-deep.html | https://neurarch.com/a/wide-and-deep.md | https://neurarch.com/templates/wide-and-deep/model.json | https://neurarch.com/?template=wide-and-deep |
dlrm | DLRM | Recommendation | Meta's Deep Learning Recommendation Model — bottom MLP for dense, embedding for sparse, feature interaction, top MLP | Pick when you have substantial dense + sparse features and need a production-validated CTR baseline. Bare-bones — needs feature engineering. | 14 | 32,347,553 | 32.35M | [
13
] | [
32,
1
] | pass | [
{
"rule": "output-activation",
"severity": "info"
},
{
"rule": "vanishing-gradient",
"severity": "info"
},
{
"rule": "deep-no-norm",
"severity": "info"
},
{
"rule": "init-activation-mismatch",
"severity": "info"
}
] | https://neurarch.com/a/dlrm.html | https://neurarch.com/a/dlrm.md | https://neurarch.com/templates/dlrm/model.json | https://neurarch.com/?template=dlrm |
neumf | Neural Collaborative Filtering | Recommendation | He et al. 2017 NeuMF — GMF + MLP fused for recommendation | Pick for pure user/item collaborative filtering when you have no side features. Fused GMF+MLP variant; concat-only variant is `ncf`. | 16 | 105,610,401 | 105.61M | [
1
] | [
1,
1
] | pass | [
{
"rule": "output-activation",
"severity": "info"
},
{
"rule": "vanishing-gradient",
"severity": "info"
},
{
"rule": "deep-no-norm",
"severity": "info"
},
{
"rule": "init-activation-mismatch",
"severity": "info"
}
] | https://neurarch.com/a/neumf.html | https://neurarch.com/a/neumf.md | https://neurarch.com/templates/neumf/model.json | https://neurarch.com/?template=neumf |
graph-sage-rec | GraphSAGE Recommender | Recommendation | Inductive node embeddings via neighbor sampling + aggregation — for graph-based recommenders (PinSage style) | Pick when your recsys has a rich item-item or user-item graph and cold-start items must generalize via neighbors (PinSage-style production setup). | 10 | 263,040 | 263.0K | [
128
] | [
128,
64
] | warn | [
{
"rule": "bn-at-output",
"severity": "warn"
}
] | https://neurarch.com/a/graph-sage-rec.html | https://neurarch.com/a/graph-sage-rec.md | https://neurarch.com/templates/graph-sage-rec/model.json | https://neurarch.com/?template=graph-sage-rec |
ncf | Neural Collaborative Filtering | Recommendation | He et al. 2017 NCF — user/item embeddings → concat → MLP → score (concat-then-MLP variant) | Pick as the simplest deep CF baseline before reaching for fused (NeuMF) or graph-based variants. No feature engineering required. | 12 | 35,206,273 | 35.21M | [
1
] | [
1,
1
] | pass | [
{
"rule": "output-activation",
"severity": "info"
},
{
"rule": "vanishing-gradient",
"severity": "info"
},
{
"rule": "deep-no-norm",
"severity": "info"
},
{
"rule": "init-activation-mismatch",
"severity": "info"
}
] | https://neurarch.com/a/ncf.html | https://neurarch.com/a/ncf.md | https://neurarch.com/templates/ncf/model.json | https://neurarch.com/?template=ncf |
lightgcn | LightGCN | Recommendation | He et al. 2020 — user/item embeddings propagated through 3 light graph-conv layers, layer combination via mean (no transforms, no nonlinearities) | Pick for collaborative filtering with implicit feedback (clicks, plays). Strips graph-conv to its essentials — often beats heavier GCN variants on rec benchmarks. | 14 | 70,424,960 | 70.42M | [
1
] | [
64
] | pass | [
{
"rule": "deep-no-norm",
"severity": "info"
}
] | https://neurarch.com/a/lightgcn.html | https://neurarch.com/a/lightgcn.md | https://neurarch.com/templates/lightgcn/model.json | https://neurarch.com/?template=lightgcn |
bst | Behavior Sequence Transformer | Recommendation | Alibaba 2019 BST — user behavior sequence + target → Transformer encoder + concat with user features → MLP → CTR | Pick for sequential CTR when user behavior history matters (session intent). Production-deployed at Alibaba scale; needs sequence-aware features. | 20 | 128,756,259 | 128.76M | [
50
] | [
1
] | pass | [
{
"rule": "output-activation",
"severity": "info"
},
{
"rule": "vanishing-gradient",
"severity": "info"
},
{
"rule": "deep-no-norm",
"severity": "info"
},
{
"rule": "consecutive-linear-no-activation",
"severity": "info"
},
{
"rule": "init-activation-mismatch",
... | https://neurarch.com/a/bst.html | https://neurarch.com/a/bst.md | https://neurarch.com/templates/bst/model.json | https://neurarch.com/?template=bst |
sli-rec | SLi-Rec | Recommendation | Yu et al. 2019 — Short and Long-term Interest Recommender. Time-LSTM + ASVD attention fused via gate | Pick when both short-session intent and long-term preferences matter and you want each modelled separately, then fused via a learned gate. | 20 | 64,049,986 | 64.05M | [
50
] | [
1
] | warn | [
{
"rule": "output-activation",
"severity": "info"
},
{
"rule": "attention-no-pe",
"severity": "warn"
},
{
"rule": "vanishing-gradient",
"severity": "info"
},
{
"rule": "vanishing-gradient",
"severity": "info"
},
{
"rule": "deep-no-norm",
"severity": "info"
}... | https://neurarch.com/a/sli-rec.html | https://neurarch.com/a/sli-rec.md | https://neurarch.com/templates/sli-rec/model.json | https://neurarch.com/?template=sli-rec |
diffusion-unet | Diffusion UNet | Generative | Stable-Diffusion-style noise predictor — latent UNet with cross-attention to a text embedding | Pick when you want to generate images from a text prompt. The full pipeline also needs a VAE encoder/decoder and a text encoder (e.g. CLIP); this template is the denoiser core. | 19 | 6,687,044 | 6.69M | [
4,
64,
64
] | [
4,
64,
64
] | pass | [] | https://neurarch.com/a/diffusion-unet.html | https://neurarch.com/a/diffusion-unet.md | https://neurarch.com/templates/diffusion-unet/model.json | https://neurarch.com/?template=diffusion-unet |
deepseek-v3 | DeepSeek-V3 | NLP/LLM | 671B MoE LLM — Multi-head Latent Attention (MLA) compresses the KV cache; fine-grained MoE with shared + routed experts (DeepSeek 2024) | Study a frontier MoE design: MLA for cheap long-context inference and shared-expert routing. Fold collapses the 61 repeated decoder layers to one block; expand to see the full depth. | 372 | 666,329,115,904 | 666.33B | [
1,
163840
] | [
1,
163840,
129280
] | pass | [
{
"rule": "deep-attention-default-init",
"severity": "info"
}
] | https://neurarch.com/a/deepseek-v3.html | https://neurarch.com/a/deepseek-v3.md | https://neurarch.com/templates/deepseek-v3/model.json | https://neurarch.com/?template=deepseek-v3 |
llama-4-scout | Llama-4 Scout | NLP/LLM | 109B natively-multimodal MoE LLM — interleaved dense/MoE layers (16 experts) with iRoPE (interleaved no-RoPE) for long context (Meta 2025) | Reference the newest open MoE decoder: alternating dense and expert-routed blocks. A strong base for studying sparse-activation LLMs. | 434 | 102,547,680,704 | 102.55B | [
1,
10485760
] | [
1,
10486336,
202048
] | warn | [
{
"rule": "attention-no-pe",
"severity": "warn"
},
{
"rule": "moe-no-aux-loss",
"severity": "info"
},
{
"rule": "huge-linear-params",
"severity": "warn"
},
{
"rule": "deep-attention-default-init",
"severity": "info"
}
] | https://neurarch.com/a/llama-4-scout.html | https://neurarch.com/a/llama-4-scout.md | https://neurarch.com/templates/llama-4-scout/model.json | https://neurarch.com/?template=llama-4-scout |
jamba | Jamba | NLP/LLM | Hybrid SSM-Transformer-MoE — interleaves Mamba, attention, and MoE blocks in one stack (AI21 2024) | Pick to see three paradigms in one compact graph: state-space, attention, and mixture-of-experts. Good for studying hybrid long-context designs. | 53 | 13,030,961,152 | 13.03B | [
1,
4096
] | [
1,
4096,
65536
] | warn | [
{
"rule": "attention-no-pe",
"severity": "warn"
},
{
"rule": "moe-no-aux-loss",
"severity": "info"
}
] | https://neurarch.com/a/jamba.html | https://neurarch.com/a/jamba.md | https://neurarch.com/templates/jamba/model.json | https://neurarch.com/?template=jamba |
qwen3-8b | Qwen3-8B | NLP/LLM | Modern dense decoder LLM — GQA with QK-RMSNorm on the query/key projections for training stability (Alibaba 2025) | A clean, current dense-decoder reference. Pick when you want a straightforward modern LLM block without MoE routing. | 221 | 8,190,878,080 | 8.19B | [
1,
40960
] | [
1,
40960,
151936
] | warn | [
{
"rule": "attention-no-pe",
"severity": "warn"
},
{
"rule": "deep-attention-default-init",
"severity": "info"
}
] | https://neurarch.com/a/qwen3-8b.html | https://neurarch.com/a/qwen3-8b.md | https://neurarch.com/templates/qwen3-8b/model.json | https://neurarch.com/?template=qwen3-8b |
dit-xl2 | DiT-XL/2 | Generative | Diffusion Transformer — replaces the UNet denoiser with a ViT backbone conditioned on timestep + class via adaLN-Zero (Peebles 2023) | Pick when you want the modern transformer-based image-generation backbone (used by Sora/SD3-era models) instead of a convolutional UNet denoiser. | 204 | 670,684,064 | 670.68M | [
4,
32,
32
] | [
256,
32
] | pass | [
{
"rule": "deep-attention-default-init",
"severity": "info"
}
] | https://neurarch.com/a/dit-xl2.html | https://neurarch.com/a/dit-xl2.md | https://neurarch.com/templates/dit-xl2/model.json | https://neurarch.com/?template=dit-xl2 |
swin-tiny | Swin-Tiny | Computer Vision | Hierarchical vision transformer — shifted-window attention builds a feature pyramid for dense prediction (Liu 2021) | Pick when you need a ViT that produces multi-scale features (detection, segmentation) rather than a single-scale ViT. Windowed attention keeps compute linear in image size. | 81 | 28,260,808 | 28.26M | [
3,
224,
224
] | [
1000
] | warn | [
{
"rule": "attention-no-pe",
"severity": "warn"
},
{
"rule": "deep-attention-default-init",
"severity": "info"
}
] | https://neurarch.com/a/swin-tiny.html | https://neurarch.com/a/swin-tiny.md | https://neurarch.com/templates/swin-tiny/model.json | https://neurarch.com/?template=swin-tiny |
clip-vit-b32 | CLIP ViT-B/32 | Multimodal | Dual-encoder contrastive model — a ViT image tower and a Transformer text tower projected into a shared embedding space (OpenAI 2021) | Pick to study cross-modal retrieval / zero-shot classification. Two parallel encoders meet at a contrastive similarity head. | 38 | 151,198,976 | 151.20M | [
3,
224,
224
] | [
512
] | pass | [] | https://neurarch.com/a/clip-vit-b32.html | https://neurarch.com/a/clip-vit-b32.md | https://neurarch.com/templates/clip-vit-b32/model.json | https://neurarch.com/?template=clip-vit-b32 |
llava-1.5-7b | LLaVA-1.5 | Multimodal | Vision-language model — CLIP image encoder + MLP projector feed visual tokens into a LLaMA decoder (Liu 2023) | Pick to see the canonical VLM recipe: a frozen vision encoder bridged into an LLM by a small projector. Foundation for image chat / VQA. | 229 | 7,062,339,840 | 7.06B | [
3,
336,
336
] | [
1,
2624,
32000
] | warn | [
{
"rule": "full-mha-serving-cost",
"severity": "info"
},
{
"rule": "deep-attention-default-init",
"severity": "info"
},
{
"rule": "kv-cache-context-budget",
"severity": "warn"
}
] | https://neurarch.com/a/llava-1.5-7b.html | https://neurarch.com/a/llava-1.5-7b.md | https://neurarch.com/templates/llava-1.5-7b/model.json | https://neurarch.com/?template=llava-1.5-7b |
Neurarch architecture corpus
36 neural network architectures kept as typed graphs rather than diagrams. Each entry carries every layer's type and parameters, the tensor shape propagated through it, an estimated parameter count, the verdict of 41 structural checks, and a link to the model.json an agent can fetch, edit and submit back for verification. Families span vision, language, recommendation, diffusion, biosignal and speech.
- Size: 36 architectures
- Licence: CC0 1.0 Universal
- Canonical page: https://neurarch.com/d/architectures.html
- On the site: https://neurarch.com/a/
What the viewer shows
The viewer shows one row per architecture. The full graphs are the 36 files under graphs/, which are deliberately not a table: a conv graph and a linear graph do not share a schema, and globbing them is what breaks the viewer.
Fields
- layer count
- estimated parameter count
- input shape
- output shape
- verifier verdict
What this dataset is not
Every graph here passes our own verifier by construction: a template with a blocking finding is held back from the gallery rather than published. That makes this a corpus of designs that are structurally sound, not a sample of designs people actually write, and the wrong set to measure a checker's recall against. The grounding study is the one with failures in it.
Verify a design of your own
These rows describe neural network graphs that were checked by a deterministic verifier, and the same verifier is callable:
curl -X POST https://www.neurarch.com/api/v1/check \
-H "Authorization: Bearer $NEURARCH_API_KEY" \
-H "Content-Type: application/json" \
-d @graph.json
Cite
Neurarch. Neurarch architecture corpus. https://neurarch.com/d/architectures.html
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