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Auto-Labeling — Interpreting Features at Scale

With 8,000–131,000 features, manual labeling is impractical. miStudio's auto-labeling system uses LLMs to interpret each feature from its activation examples.

miStudio provides two labeling paths:

PathScopeWhen to Use
Bulk Auto-Labeling (this page)All features in one jobInitial survey — generate a name + category for every feature quickly
Enhanced LabelingOne feature at a timeDeep analysis — structured two-pass interpretation with full reasoning notes

Bulk Auto-Labeling​

Labeling Configuration Panel — Method selection and options

Four Labeling Methods​

MethodCostSpeedPrivacyBest For
OpenAI$$$FastCloudHighest quality labels. gpt-4o-mini is cost-effective.
OpenAI-CompatibleFree–$VariableLocal/CloudLocal models via Ollama, vLLM, miLLM, or any OpenAI-compatible API
LocalFreeSlowFullHuggingFace models loaded directly. Complete privacy.
ManualFreeSlowestFullHuman-provided labels for verification or correction
OpenAI API Key Setup

To use the OpenAI method, set your API key once in Settings → API Keys. It is stored encrypted at rest (AES-256-GCM) and auto-used by all labeling jobs — you don't need to re-enter it per job.

OpenAI-Compatible Endpoints

The most flexible option. Point it at any OpenAI-compatible API:

  • Ollama: http://localhost:11434/v1 (local, free)
  • miLLM: http://millm-backend:8000/v1 (local, free, GPU-accelerated)
  • vLLM: http://localhost:8000/v1 (local, high throughput)
  • Together AI, Fireworks, etc.: Cloud providers with OpenAI-compatible APIs

Save your endpoint and model once in Settings → Endpoints, then select it when starting a labeling job.

Labeling Configuration​

ParameterDefaultRangeEffect
Batch Size101–100Features labeled in parallel. Higher = faster but may overwhelm local models.
Max Examples2510–50Activation examples shown to the LLM per feature. More = better context but longer prompts.
Max Tokens30050–8,000Maximum response length from LLM. Increase for reasoning models that use <think> tags.
API Timeout120s30–600sRequest timeout. Increase for large local models.
Reasoning Models

Models like LFM2.5-Thinking or DeepSeek-R1 produce <think>...</think> tags before their answer. miStudio automatically strips these. Set max_tokens to 1,000–2,000 to ensure the actual answer isn't truncated after the thinking phase.

Protecting Enhanced Labels​

If you have used Enhanced Labeling on specific features, those features display an aqua star (🔵). Bulk auto-labeling jobs automatically skip aqua-starred features — so a bulk run won't overwrite your carefully enhanced labels.

Features receiving new bulk labels show no star or a label-source badge indicating the labeling method used.

Labeling Progress & Results​

Once a labeling job is running, track progress in real-time:

Labeling Job Progress — Real-time progress and labeled results

Browse completed labeling results across all jobs:

Labeling Results Browser — View and compare labels across jobs

The Label System​

Every feature receives:

  • Name: A descriptive slug (e.g., legal_precedent_citation, source_attribution_from)
  • Category: A high-level classification (e.g., semantic, syntactic, positional, discourse)
  • Description: (Enhanced labeling only) One precise sentence grounding the pattern
  • Notes: (Enhanced labeling only) Full reasoning + per-example summary table

Labels track their provenance — whether they came from OpenAI, a local model, enhanced labeling, or manual editing — enabling quality comparison across methods.

Prompt Templates​

The quality of bulk labels depends heavily on what you ask the LLM to do. miStudio ships with several system templates, and you can create custom ones.

The built-in "Context-Aware Labeling (Semantic Pattern)" template produces noticeably better labels by shifting the LLM's frame from token-naming to semantic pattern recognition.

Standard templates ask: "What token does this feature fire on?"
This produces labels like article_the or preposition_from — technically accurate but informationally shallow.

Context-Aware template asks: "What is semantically happening across ALL these examples?"
This produces labels like definite_reference_introduction or source_attribution_legal — names that tell you what the feature means, not just where it fires.

When Does It Matter?

Many features fire on common tokens (the, and, of) but encode completely different things. The preposition from can encode source attribution, temporal origin, departure point, or comparison contrast — these are separate features that need separate names. The context-aware template reliably finds these distinctions.

To use it: in the Start Labeling dialog, select "Context-Aware Labeling (Semantic Pattern)" from the Template dropdown.

Start Semantic Labeling dialog — Template dropdown, Method selector, and model configuration

Creating Custom Templates​

In Labeling → Templates, you can:

  • Create templates with custom system and user prompts
  • Use {examples_block} to inject full context windows (prefix + token + suffix per example)
  • Set temperature, max tokens, and include/exclude negative examples
  • Save and reuse across labeling jobs

See The Template Ecosystem for full details.