--- license: cc-by-nc-4.0 --- # 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: | Gemma 2 Model | SAE Width | Attention | MLP | Residual | Tokens | |---------------|-----------|-----------|-----|----------|----------| | 2.6B PT
(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
(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
(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
(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 |