beethovain 0.3.1

AI Model Blending Toolkit — orchestrate multiple models for superior performance

Beethovain

AI Model Blending Toolkit — Orchestrate multiple models for superior performance.

Blend small language models (SLMs) together to achieve results that rival larger models, at a fraction of the cost.

Quick Start

pip install beethovain

from beethovain import BlendSession

session = BlendSession.from_yaml("experiment.yaml")
result = await session.blend("Classify this article", "judge")
print(result.parsed)  # {"is_benefit": true, "confidence": 0.82}

What is Blending?

Instead of using one large model, combine multiple smaller models:

• Routing — Fast model first, escalate to strong model if confidence is low
• Ensemble — Multiple models vote, weighted majority wins
• Cascade — Primary generates, secondary verifies/corrects

Real result: 3 SLMs (1.2B + 4B + 7.8B) blended → F1 80.1% on a classification task.

Features

Feature	Command
SDK	from beethovain import BlendSession
CLI	beethovain blend config.yaml judge --prompt "..."
Record	beethovain record --function sfm --models colmap --project "..." — log any experiment (vision/3D/training too)
MCP	beethovain mcp — AI agents record/query/push as MCP tools
REST API	beethovain serve config.yaml
Dashboard	Browser → http://localhost:8000
TUI	beethovain watch
Push	beethovain push --project "MyProject" [--private] → beethovain.com

For AI Agents (MCP)

pip install beethovain[mcp]
claude mcp add beethovain -- beethovain mcp

Exposes record_experiment / list_experiments / get_experiment / get_stats / push_to_hub. Working in another project and told to "use beethovain"? → read AGENT_GUIDE.md.

Projects & Runs (like GitHub repos & commits)

Every run belongs to a project. Set it via --project, the YAML project: key, or BlendSession.from_yaml(..., project=...). beethovain.com lists projects, not raw runs.

YAML Config

judge:
  strategy: ensemble
  models:
    - name: "exaone3.5:7.8b"
      tier: strong
      weight: 0.95
    - name: "gemma3:1b"
      tier: fast
      weight: 0.6
  min_agreement: 2
  result_type: binary

Requirements

• Python 3.11+
• Ollama running locally (for model inference)

Optional Dependencies

pip install beethovain[api]   # REST API + Dashboard
pip install beethovain[tui]   # Terminal dashboard
pip install beethovain[hf]    # HuggingFace models
pip install beethovain[all]   # Everything

Links

• Website: beethovain.com
• Docs: Coming soon