SAE Management

The SAEs panel is the unified home for every sparse autoencoder in your workspace, regardless of where it came from:

• Trained — SAEs produced by miStudio training jobs
• HuggingFace — pre-trained SAEs downloaded from the Hub (including Gemma Scope)
• Local — SAE files imported from disk

Each card shows the SAE's base model, layer, hook type, dimensions (d_model → n_features), architecture, and source. From here you can launch feature extraction, open steering, export to Neuronpedia, or delete.

Downloading Pre-Trained SAEs

Enter a HuggingFace repository ID and preview the available SAE files before downloading:

• Multi-select downloads: select several SAEs from one repository and download them in a single operation
• Grouped preview: files are organized by directory structure so multi-layer repos stay navigable
• Compatibility check: dimensions are validated against the base model before extraction

Gemma Scope

Google's Gemma Scope repositories follow a layer_N/width_16k/average_l0_XX/ layout. miStudio parses this structure automatically — pick the layer, width, and L0 variant you want, and the SAE is stored locally in SAELens community format.

Importing SAEs

Stopped runs can be imported too

A run that was stopped early has no SAE to import until it is finalized. Click Finalize on the training, then import as usual — see Training Lifecycle & Checkpoints.

Two import paths complement HF downloads:

• Import from training — register the SAE(s) a completed miStudio training produced. Multi-layer/multi-hook trainings expose all of their SAEs for import; already-imported ones appear disabled in the picker so you can't double-import. The hook type is auto-detected from each SAE's cfg.json.
• Import from file — point at an SAE directory already on disk.

Formats and Conversion

miStudio reads and writes two formats and converts between them automatically during download/import:

Format	Layout	Used by
SAELens community standard	cfg.json + sae_weights.safetensors	Gemma Scope, most published SAEs
miStudio native	config.json + model.safetensors (+ training metadata)	miStudio training output

Format detection is automatic — you never select a format manually.

Extracting Features from an SAE

Every SAE card has an Extract Features action that launches the SAE→features pipeline described in Feature Extraction. Extraction progress streams to the card, and in-flight extractions can be cancelled. A batch extract action processes several SAEs sequentially.

Delete Semantics

Deleting an SAE offers two behaviors:

Option	What happens	Reversible?
Delete with files (default)	Hard delete — removes the SAE record, its weight files, and cascades to all features extracted from it	No
Keep files	Soft delete — the record is marked deleted but weights stay on disk	Yes (re-import)

Cascading feature deletion

The default hard delete removes every extracted feature, label, and activation example derived from the SAE. If you've invested labeling effort, export to Neuronpedia first or use the soft-delete option.

Uploading to HuggingFace

Trained SAEs can be pushed back to the Hub for sharing. Uploads use a layer_XX/width_{n}k/ directory convention compatible with the Gemma Scope layout, so your published SAEs are browsable with the same tooling. Configure your HF token in Settings → API Keys first.