eulogik/fugusashi-router

Fugusashi Router

By eulogik — building AI infrastructure for everyone.

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What is This?

CMA-ES evolved routing weights for the Fugusashi intelligent model router. A 385-dimensional weight vector that maps prompt embeddings to model selection — learned via Covariance Matrix Adaptation Evolution Strategy, the same approach used in Sakana AI's TRINITY paper.

Like Sakana Fugu. But Free. And Yours.

Usage

import numpy as np
import json
from sentence_transformers import SentenceTransformer

# Load weights
with open("cmaes_weights.json") as f:
    data = json.load(f)

weights = np.array(data["mean"])
bias = data["mean"][-1]

# Embed prompt and route
model = SentenceTransformer("all-MiniLM-L6-v2")
embedding = model.encode(["Write a Python function to sort a list"], normalize_embeddings=True)[0]

logits = embedding * weights[:384] + bias
models = ["gpt-oss-120b", "nemotron-3-ultra", "nemotron-3-super", "hermes-3-405b", "lfm-2.5-1.2b"]
chosen = models[np.argmax(logits)]
print(f"Route to: {chosen}")

Training Details

Field	Value
Algorithm	CMA-ES (Covariance Matrix Adaptation Evolution Strategy)
Dimensions	385 (384 weights + 1 bias)
Population	16
Generations	30
Training tasks	20 preference samples
Best fitness	~0.24
Embedding model	all-MiniLM-L6-v2

Project Links

Resource	Link
🌐 Website	eulogik.github.io/fugusashi
💻 Source Code	github.com/eulogik/fugusashi
📦 PyPI	pypi.org/project/fugusashi
📊 Dataset	huggingface.co/datasets/eulogik/fugusashi-preferences
🚀 Live Demo	huggingface.co/spaces/eulogik/fugusashi
🌍 eulogik	eulogik.com

Citation

If you use Fugusashi in your work:

@software{fugusashi2026,
  title={Fugusashi: Open-Source Intelligent Model Router},
  author={{eulogik}},
  year={2026},
  url={https://github.com/eulogik/fugusashi}
}

License

MIT — use it however you want.

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**Built with ❤️ by [eulogik](https://eulogik.com)**