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    Neuronpedia

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    Neuronpedia is an open source interpretability platform.
    Explore, visualize, and steer the internals of AI models.
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    Featured Releases
    Lu et al. 2026
    Assistant Axis
    Monitor and Stabilize the Character of an LLM
    Multi-Org
    Circuit Tracer
    Trace the Internal Reasoning Steps of a Model
    Google Deepmind
    Gemma Scope 2
    SAEs and Transcoders for Gemma 3
    MIT Technology ReviewAnthropicGoogle DeepMindVentureBeatOpenMOSS, Fudan UniversityEleutherAIApollo Research
    Explore
    Browse five+ terabytes of activations, explanations, and metadata.
    Neuronpedia supports probes, latents/features, custom vectors, concepts, and more.

    Releases

    Gemma Scope 2: Comprehensive Suite of SAEs and Transcoders for Gemma 3
    Language Model Interpretability Team, Google DeepMind
    Temporal Feature Analysis
    Lubana, Rager, Hindupur, et al.
    gpt-oss BatchTopK SAEs
    Andy Arditi
    Finding Misaligned Persona Features in Open-Weight Models
    Andy Arditi
    Circuit Tracer Transcoders
    Hanna & Piotrowski
    A Bunch of Matryoshka SAEs
    David Chanin
    Llama 3.3 70B Instruct SAE
    Goodfire
    Llama Scope R1: SAEs for DeepSeek-R1-Distill-Llama-8B
    OpenMOSS Team, Fudan University
    Gemma Scope - Exploring the Inner Workings of Gemma 2
    Language Model Interpretability Team, Google DeepMind
    AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders
    pyvene.ai, The Stanford NLP Group
    Llama Scope: SAEs for Llama-3.1-8B
    OpenMOSS Team, Fudan University
    Feature Splitting for GPT2-Small
    Joseph Bloom
    Multi TopK SAE for Llama3.1-8B
    EleutherAI
    Sparse Autoencoder for GPT2-Small - v5
    OpenAI
    Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning
    Apollo Research · Jordan Taylor
    Transcoders Enable Fine-Grained Interpretable Circuit Analysis for Language Models
    Jacob Dunefsky · Philippe Chlenski
    Sparse Autoencoders for Pythia-70M-Deduped
    Under Peer Review
    Attention SAE Research Paper
    Under Peer Review
    Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
    Joseph Bloom

    Models

    CIRCUITGPT-PYTHON
    CircuitGPT-PythonOpenAI
    GEMMA-3-27B
    Gemma-3-27BGoogle Deepmind
    GEMMA-3-12B
    Gemma-3-12BGoogle Deepmind
    GEMMA-3-270M-IT
    Gemma-3-270M-ITGoogle Deepmind
    GEMMA-3-1B-IT
    Gemma-3-1B-ITGoogle Deepmind
    GEMMA-3-4B-IT
    Gemma-3-4B-ITGoogle Deepmind
    GEMMA-3-12B-IT
    Gemma-3-12B-ITGoogle Deepmind
    GEMMA-3-27B-IT
    Gemma-3-27B-ITGoogle Deepmind
    GEMMA-3-270M
    Gemma-3-270MGoogle Deepmind
    GEMMA-3-4B
    Gemma-3-4BGoogle Deepmind
    GEMMA-3-1B
    Gemma-3-1BGoogle Deepmind
    GEMMA-2-27B
    Gemma-2-27BGoogle Deepmind
    GPT-OSS-20B
    GPT-OSS-20BOpenAI
    QWEN2.5-7B-IT
    Qwen2.5-7B-ITAlibaba
    LLAMA3.1-8B-IT
    Llama3.1-8B-IT (Instruct)Meta
    Qwen3-1.7B
    Qwen3-1.7BAlibaba
    QWEN3-4B
    Qwen3-4BAlibaba
    LLAMA3.3-70B-IT
    Llama3.3-70B-IT (Instruct)Meta
    DEEPSEEK-R1-LLAMA-8B
    DeepSeek-R1-Dist-Llama-8BDeepSeek
    GEMMA-2-2B-IT
    Gemma-2-2B-ITGoogle Deepmind
    GEMMA-2-9B-IT
    Gemma-2-9B-ITGoogle Deepmind
    LLAMA3.1-8B
    Llama3.1-8B (Base)Meta
    GEMMA-2-2B
    Gemma-2-2BGoogle Deepmind
    GEMMA-2-9B
    Gemma-2-9BGoogle Deepmind
    P70M-D
    Pythia-70M-DedupedEleutherAI
    GPT2-SMALL
    GPT2-SmallOpenAI

    Jump To

    Jump to Source/SAE
    Jump to Feature
    INDEX
    Jump to Random
    Graph
    Visualize and trace the internal reasoning steps of a model with custom prompts, pioneered by Anthropic's circuit tracing papers.
    Attribution graph example with Dallas gemma-2-2b
    Steer
    Modify model behavior by steering its activations using latents or custom vectors. Steering supports instruct (chat) and reasoning models, and has fully customizable temperature, strength, seed, etc.
    Steering example with a cat feature
    Search
    Search over 50,000,000 latents/vectors, either by semantic similarity to explanation text, or by running custom text via inference through a model to find top matches.

    Search via Inference

    Run Example Search
    API + Libraries
    Neuronpedia hosts the world's first interpretability API (March 2024) - and all functionality is available by API or Python/TypeScript libraries. Most endpoints have an OpenAPI spec and interactive docs.
    Steering example with a cat feature
    Inspect
    Go in depth on each probe/latent/feature with top activations, top logits, activation density, and live inference testing. All dashboards have unique links, can be compiled into sharable lists, and supports IFrame embedding, as demonstrated here.
    Who We Are
    Neuronpedia was created by Johnny Lin, an ex-Apple engineer who previously founded a privacy startup. Neuronpedia is supported by Decode Research, Open Philanthropy, the Long Term Future Fund, AISTOF, Anthropic, Manifund, and others.
    Get Involved
    Citation
    @misc{neuronpedia,
    title = {Neuronpedia: Interactive Reference and Tooling for Analyzing Neural Networks},
    year = {2023},
    note = {Software available from neuronpedia.org},
    url = {https://www.neuronpedia.org},
    author = {Lin, Johnny}
}