README.md

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license: cc-by-nc-4.0
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# Gemma Scope:

!(Gemma Scope 002 - 16-9.gif)

This is a landing page for **Gemma Scope**, a comprehensive, open suite of sparse autoencoders for Gemma 2 9B and 2B. Sparse Autoencoders are a "microscope" of sorts that can help us break down a model’s internal activations into the underlying concepts, just as biologists use microscopes to study the individual cells of plants and animals.

# Key links:

- Learn more about Gemma Scope on the Google DeepMind blog (TODO link).
- Check out the [interactive Gemma Scope demo](https://www.neuronpedia.org/gemma-scope) made by [Neuronpedia](https://www.neuronpedia.org/).
- Check out our [Google Colab notebook tutorial](https://colab.research.google.com/drive/17dQFYUYnuKnP6OwQPH9v_GSYUW5aj-Rp?ts=66a77041) for how to use Gemma Scope.
- Read the Gemma Scope technical report (TODO link).
- Check out Mishax, an internal tool we used to help make Gemma Scope (TODO link).

# Quick start:

You can get started with Gemma Scope by downloading the weights from any of our repositories:

- https://huggingface.co/google/gemma-scope-2b-pt-res
- https://huggingface.co/google/gemma-scope-2b-pt-mlp
- https://huggingface.co/google/gemma-scope-2b-pt-att
- https://huggingface.co/google/gemma-scope-2b-pt-transcoders
- https://huggingface.co/google/gemma-scope-9b-pt-res
- https://huggingface.co/google/gemma-scope-9b-pt-mlp
- https://huggingface.co/google/gemma-scope-9b-pt-att
- https://huggingface.co/google/gemma-scope-9b-it-res
- https://huggingface.co/google/gemma-scope-27b-pt-res

The full list of SAEs we trained at which sites and layers are linked from the following table, adapted from Figure 1 of our technical report:

| <big>Gemma 2 Model</big> | <big>SAE Width</big> | <big>Attention</big> | <big>MLP</big> | <big>Residual</big> | <big>Tokens</big> |
|---------------|-----------|-----------|-----|----------|----------|
| 2.6B PT<br>(26 layers) | 2^14 ≈ 16.4K | [All](https://huggingface.co/google/gemma-scope-2b-pt-att) | [All](https://huggingface.co/google/gemma-scope-2b-pt-mlp)[+](https://huggingface.co/google/gemma-scope-2b-pt-transcoders) | [All](https://huggingface.co/google/gemma-scope-2b-pt-res) | 4B |
| | 2^15 |  |  | {[12](https://huggingface.co/google/gemma-scope-2b-pt-res/tree/main/layer_12/width_32k/)} | 8B |
| | 2^16 | [All](https://huggingface.co/google/gemma-scope-2b-pt-att) | [All](https://huggingface.co/google/gemma-scope-2b-pt-mlp) | [All](https://huggingface.co/google/gemma-scope-2b-pt-res) | 8B |
| | 2^17 |  |  | {[12](https://huggingface.co/google/gemma-scope-2b-pt-res/tree/main/layer_12/width_131k/)} | 8B |
| | 2^18 |  |  | {[12](https://huggingface.co/google/gemma-scope-2b-pt-res/tree/main/layer_12/width_262k/)} | 8B |
| | 2^19 |  |  | {[12](https://huggingface.co/google/gemma-scope-2b-pt-res/tree/main/layer_12/width_524k/)} | 8B |
| | 2^20 ≈ 1M |  |  | {[5](https://huggingface.co/google/gemma-scope-2b-pt-res/tree/main/layer_5/width_1m/), [12](https://huggingface.co/google/gemma-scope-2b-pt-res/tree/main/layer_12/width_1m/), [19](https://huggingface.co/google/gemma-scope-2b-pt-res/tree/main/layer_19/width_1m/)} | 16B |
| 9B PT<br>(42 layers) | 2^14 | [All](https://huggingface.co/google/gemma-scope-9b-pt-att) | [All](https://huggingface.co/google/gemma-scope-9b-pt-mlp) | [All](https://huggingface.co/google/gemma-scope-9b-pt-res) | 4B |
| | 2^15 |  |  | {[20](https://huggingface.co/google/gemma-scope-9b-pt-res/tree/main/layer_20/width_32k/)} | 8B |
| | 2^16 |  |  | {[20](https://huggingface.co/google/gemma-scope-9b-pt-res/tree/main/layer_20/width_65k/)} | 8B |
| | 2^17 | [All](https://huggingface.co/google/gemma-scope-9b-pt-att) | [All](https://huggingface.co/google/gemma-scope-9b-pt-mlp) | [All](https://huggingface.co/google/gemma-scope-9b-pt-res) | 8B |
| | 2^18 |  |  | {[20](https://huggingface.co/google/gemma-scope-9b-pt-res/tree/main/layer_20/width_262k/)} | 8B |
| | 2^19 |  |  | {[20](https://huggingface.co/google/gemma-scope-9b-pt-res/tree/main/layer_20/width_524k/)} | 8B |
| | 2^20 |  |  | {[9](https://huggingface.co/google/gemma-scope-9b-pt-res/tree/main/layer_9/width_1m/), [20](https://huggingface.co/google/gemma-scope-9b-pt-res/tree/main/layer_20/width_1m/), [31](https://huggingface.co/google/gemma-scope-9b-pt-res/tree/main/layer_31/width_1m/)} | 16B |
| 27B PT<br>(46 layers) | 2^17 |  |  | {[10](https://huggingface.co/google/gemma-scope-27b-pt-res/tree/main/layer_10/width_131k/), [22](https://huggingface.co/google/gemma-scope-27b-pt-res/tree/main/layer_22/width_131k/), [34](https://huggingface.co/google/gemma-scope-27b-pt-res/tree/main/layer_34/width_131k/)} | 8B |
| 9B IT<br>(42 layers) | 2^14 |  |  | {[9](https://huggingface.co/google/gemma-scope-9b-it-res/tree/main/layer_9/width_16k/), [20](https://huggingface.co/google/gemma-scope-9b-it-res/tree/main/layer_20/width_16k/), [31](https://huggingface.co/google/gemma-scope-9b-it-res/tree/main/layer_31/width_16k/)} | 4B |
| | 2^17 |  |  | {[9](https://huggingface.co/google/gemma-scope-9b-it-res/tree/main/layer_9/width_131k/), [20](https://huggingface.co/google/gemma-scope-9b-it-res/tree/main/layer_20/width_131k/), [31](https://huggingface.co/google/gemma-scope-9b-it-res/tree/main/layer_31/width_131k/)} | 8B |