Architectures / NLP/LLM

🦙 Llama-4 Scout

109B natively-multimodal MoE LLM — interleaved dense/MoE layers (16 experts) with iRoPE (interleaved no-RoPE) for long context (Meta 2025)

Every number on this page is computed from the graph by the same functions the app runs, not written by hand.

When to pick it

Structure

434 layers. Output shapes are propagated from the input shape, batch dimension excluded.

What the verifier says

The same 43 single-graph checks that run on every edit in the app, on this graph.

The PyTorch it exports

Generated from the graph above. First 46 lines; the app exports the whole file, plus the training loop, the data contract and a deploy bundle.

# Architecture designed with Neurarch: https://neurarch.com
# PyTorch: compatible with Python 3.8+ and torch>=1.12
# Colab: pip install torch torchvision  (usually pre-installed)

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple

class Llama4Scout(nn.Module):
    def __init__(self):
        super().__init__()

        self.embedding_1 = nn.Embedding(202048, 5120)
        self.patchEmbed_1 = nn.Conv2d(3, 1408, kernel_size=14, stride=14)  # Patch embedding (ViT-style)
        self.layerNorm_1 = nn.LayerNorm(1408)
        self.multiHeadAttention_1 = nn.MultiheadAttention(embed_dim=1408, num_heads=16, batch_first=True)
        self.feedForward_1 = nn.Sequential(
            nn.Linear(1408, 5632),
            nn.ReLU(),
            nn.Linear(5632, 1408)
        )
        self.layerNorm_2 = nn.LayerNorm(1408)
        self.multiHeadAttention_2 = nn.MultiheadAttention(embed_dim=1408, num_heads=16, batch_first=True)
        self.feedForward_2 = nn.Sequential(
            nn.Linear(1408, 5632),
            nn.ReLU(),
            nn.Linear(5632, 1408)
        )
        self.layerNorm_3 = nn.LayerNorm(1408)
        self.multiHeadAttention_3 = nn.MultiheadAttention(embed_dim=1408, num_heads=16, batch_first=True)
        self.feedForward_3 = nn.Sequential(
            nn.Linear(1408, 5632),
            nn.ReLU(),
            nn.Linear(5632, 1408)
        )
        self.layerNorm_4 = nn.LayerNorm(1408)
        self.multiHeadAttention_4 = nn.MultiheadAttention(embed_dim=1408, num_heads=16, batch_first=True)
        self.feedForward_4 = nn.Sequential(
            nn.Linear(1408, 5632),
            nn.ReLU(),
            nn.Linear(5632, 1408)
        )
        self.layerNorm_5 = nn.LayerNorm(1408)
        self.multiHeadAttention_5 = nn.MultiheadAttention(embed_dim=1408, num_heads=16, batch_first=True)
        self.feedForward_5 = nn.Sequential(

For agents

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