Distill any task into a pocket-sized Spirit.

Hand us a teacher API and a task spec. We distill it into a 20–50M model that runs on CPU, edge, or browser with zero API dependency at inference.

Start a Distillation Browse the Cellar

$0.30

Avg distillation cost

27m

Median run time

20M

Typical Spirit size

TeacherGemini 2.5

Examples0

Proof0

Yield0 ml

⚗ The Process

A real distillation, in our language. Each step maps to something concrete in the engine.

🌾

The Mash

Define a task. We seed the corpus with prompts your teacher will riff on.

= training data spec

📜

The Recipe

Pick a teacher (Claude, Gemini, GPT), student arch, eval criteria. Save & fork.

= versioned config

🔥

The Still

We distill. Live progress, fluid sim, loss curve, drip counter — all real.

= the training run

📝

Tasting Notes

Auto-generated eval report. Strengths, weaknesses, failure cases, vs. teacher.

= held-out metrics

🍾

Bottling

Export to ONNX, GGUF, or browser-WASM. Signed, versioned, deployable.

= deployment artifact

▶ Try It

Install the CLI and distill your first Spirit. About 30 minutes on a single GPU, ~$0.30 in teacher API.

# Install
pip install distillarium[gemini]

# Distill — uses GOOGLE_API_KEY from env
distillery distill recipes/needle.tool-calling-v1.yaml

# Inspect your local Cellar
distillery cellar

# Re-taste against fresh held-out data
distillery taste spirits/needle.pt --mash held_out.jsonl

# Bottle for deployment (ONNX, GGUF, WASM)
distillery bottle spirits/needle.pt --format onnx