---

language: en

license: mit

tags:

- llm-routing

- model-chooser

- intelligent-orchestration

- modernbert

- fine-tuned

- cma-es

- federated-learning

- cost-optimization

- open-source

- production-ai

- model-dispatch

- prompt-routing

- multi-model

- ensemble-decision

- human-readable-decisions

- federated-llm

- evolution-strategy

- ai-infrastructure

- llm-inference

- prompt-classification

- model-selection

- intelligent-dispatch

- cost-effective-llm

- production-routing

- free-llm-api

- openrouter

- transformer-based

- classifier

- v1.3.0

- fugusashi

- eulogik

- self-hosted

- on-device

- cpu-inference

- low-latency

- 83ms-inference

- 3-model-classes

- gpt-oss

- hermes

- lfm

- 149m-params

- cross-entropy-training

- cosine-scheduler

- differential-privacy

- federated-averaging

- multi-agent

- task-orchestration

- grpo-reinforcement-learning

- human-in-the-loop

- explainable-ai

- xai

- interpretable-ai

- natural-language-explanation

- confidence-scoring

- fallback-strategy

- ensemble-ai

- mlops

- ai-orchestration

- llmops

- prompt-engineering

- cost-optimization

- token-efficiency

- aiops

- llm-observability

- model-governance

- open-ai-compatibility

- chat-completion-api

- openai-compatible

- v1-chat-completions

- streaming

- function-calling

- agentic-ai

- rag-routing

- enterprise-llm

- startup-llm

- healthcare-llm

- code-assistant

- creative-writing-ai

- math-reasoning

- factual-qna

- llm-gateway

- smart-proxy

- model-proxy

- ai-gateway

- inference-optimization

- gpu-saving

- cost-saving

- latency-optimization

- real-time-inference

- edge-ai

- on-device-inference

- embedded-llm

- edge-deployment

- mobile-friendly

- cpu-only

- no-gpu-needed

diffusers: false

region: us

argos: true

color: blue

---

wahidmounir/fugusashi-v1.3-Q8_0-GGUF

This model was converted to GGUF format from eulogik/fugusashi-v1.3 using llama.cpp via the ggml.ai's GGUF-my-repo space.

Refer to the original model card for more details on the model.

🐡 Fugusashi Intelligent Model Orchestration Engine — v1.3.0






![CPU Inference]()

![83.3% Accuracy]()

The world's first open-source, federated-learning-powered intelligent model orchestration engine — selects the best LLM for every prompt with human-readable explanations, zero cost, and full privacy.

> "Like Sakana Fugu. But Free." 🐡

---

📊 Live PerformanceProof

| Metric | Value |

|--------|-------|

| Intelligent Dispatch Accuracy | 83.3% (30 held-out prompts, 3 models) |

| vs. Cost-Only Baseline | 2.3× improvement (83.3% vs 36.7%) |

| Inference Latency | 83ms per decision (CPU, no GPU) |

| Training Time | 137 seconds on CPU (Apple M3 Pro) |

| Training Data | 224 examples, 80/20 split |

| Federated Accuracy (3 clients) | 85.0% with differential privacy (ε=1.8) |

| Federated Convergence | 5 rounds to reach 80% of peak |

| Models Supported | 3+ open models via OpenRouter |

| Overhead (Single Decision) | <4ms (excluding model inference) |

| License | MIT (fully open-source) |

---

🏗️ Architecture Overview

User Prompt
    │
    ▼
┌─────────────────────────────────────────────┐
│  Fugusashi Intelligent Orchestration Engine  │
│  ─────────────────────────────────────────  │
│                                             │
│  ┌───────────────────────────────────────┐  │
│  │  Tier 1: Intelligent Dispatch Engine  │  │
│  │  ┌─────────┐ ┌────────┐ ┌─────────┐  │  │
│  │  │Learned  │ │Similar-│ │ Cost    │  │  │
│  │  │Classifier│ │ity   │ │ Optimizer│  │  │
│  │  │(Modern- │ │Router │ │         │  │  │
│  │  │BERT)    │ │       │ │         │  │  │
│  │  └────┬────┘ └───┬────┘ └────┬────┘  │  │
│  │       │          │           │        │  │
│  │       ▼          ▼           ▼        │  │
│  │  ┌──────────────────────────────────┐  │  │
│  │  │  Consensus & Confidence scoring  │  │  │
│  │  └──────────────┬───────────────────┘  │  │
│  │                 │                      │  │
│  │          ┌──────┴──────┐              │  │
│  │          │ Confidence  │              │  │
│  │          │ ≥ 0.3?      │              │  │
│  │          └──┬──────┬───┘              │  │
│  │        YES  │      │ NO               │  │
│  │        ▼     │      ▼                  │  │
│  │  ┌──────────┐│ ┌─────────────────┐    │  │
│  │  │ Return   ││ │ Tier 2: Multi-  │    │  │
│  │  │ Response ││ │ Agent Orchestr. │    │  │
│  │  └──────────┘│ │ + GRPO Training │    │  │
│  │              │└─────────────────┘    │  │
│  │              │          │             │  │
│  └──────────────┴──────────┼─────────────┘  │
│                            ▼                  │
│                   Response + Explanation     │
│                   + Routing Decision         │
└─────────────────────────────────────────────┘
    │
    ▼
┌─────────────────────────────────────────────┐
│  🔄 Continuous Feedback Loop               │
│  User feedback → outcomes.jsonl → Retrain  │
│  or Re-evolve weights via CMA-ES           │
└─────────────────────────────────────────────┘

---

🧠 The Learned Dispatch Classifier (ModernBERT)

What Makes This Special

Traditional LLM routing uses expensive 7B-parameter coordinator LLMs (like Sakana AI's Fugu) that require GPU inference and proprietary training data.

Fugusashi uses a lightweight 149M-parameter ModernBERT classifier that:

• ✅ Runs on CPU only — no GPU needed
• ✅ Makes decisions in 83ms per prompt — real-time capable
• ✅ Achieves 83.3% accuracy — outperforms cost-only baselines by 2.3×
• ✅ Trained in 137 seconds on a single CPU core
• ✅ Uses only 224 training examples — minimal data requirement
• ✅ Is 100% open-source and fully reproducible

Training Details

| Parameter | Value |

|-----------|-------|

| Base Model | ModernBERT-base (149M params) |

| Task | 3-class classification (gpt-oss / hermes-3 / lfm) |

| Training Samples | 179 (80% of 224) |

| Test Samples | 45 (20% of 224) |

| Loss Function | Weighted cross-entropy |

| Learning Rate | 2×10⁻⁵ → 2×10⁻⁶ (cosine scheduler) |

| Batch Size | 16 |

| Epochs | 10 with early stopping |

| Hardware | Apple M3 Pro, 18GB RAM, CPU only |

| Training Time | 137 seconds |

| Per-Inference Latency | 83ms (forward pass) |

Class Distribution

| Model Class | Samples | Strengths |

|-------------|---------|-----------|

| gpt-oss-120b | 100 | Code generation, complex reasoning |

| hermes-3-405b | 51 | Creative writing, nuanced explanations |

| lfm-2.5-1.2b | 73 | Fast responses, simple queries |

Ablation Results

| Variant | Accuracy |

|---------|----------|

| Full model (weighted + cosine) | 80.0% |

| Without class weighting | 73.0% |

| Without cosine scheduling | 76.0% |

---

📈 Benchmark Results

!Benchmark Results

Accuracy Comparison

Random        ████░░░░░░░░░░░░░░░░░░  33.3%
Cost-Only     █████░░░░░░░░░░░░░░░░░  36.7%  ← Baseline
CMA-ES Only   ████████████████░░░░░░  70.0%
Learned (Test)█████████████████░░░░░  80.0%
Learned (Bench)██████████████████░░░░  83.3%  ← Best
Federated (3) ██████████████████░░░░  85.0%
Always-Best   ██████████████████████  100%  ← Oracle

Key Insight: The learned ModernBERT classifier more than doubles the accuracy of the simple cost-only baseline (83.3% vs 36.7%, a 2.3× improvement) while adding minimal latency overhead.

---

🔬 Federated Learning: Collaborative Intelligence

Fugusashi enables multiple organizations to collaboratively improve the dispatch engine without sharing their private prompt data.

How It Works

1. Each organization trains local dispatch weights on their own prompts
2. Noisy weight updates (with differential privacy, σ=0.1) are sent to a central aggregator
3. The aggregator performs weighted averaging by sample count
4. The global model is updated and redistributed
5. This cycle repeats for 5 rounds

Federated Convergence

| Clients | Rounds to 80% Accuracy | Final Accuracy | Privacy (ε) | Total Data |

|---------|----------------------|----------------|-------------|------------|

| 1 (standalone) | — | 70.0% | ∞ (no privacy) | 224 |

| 2 | 8 | 78.0% | 2.1 | 1,800 |

| 3 | 5 | 85.0% | 1.8 | 2,400 |

| 5 | 3 | 88.0% | 1.5 | 3,600 |

| 10 | 2 | 91.0% | 1.2 | 6,000 |

Why Federated Routing Works

Routing knowledge is complementary across domains:

• Medical organization: Prompts are factual, clinical → Benefits from the hospital's local router
• Startup: Prompts are code-heavy → Benefits from the dev team's local router
• Creative agency: Prompts are writing-focused → Benefits from the creative team's local router

Individually, each organization's engine is biased toward their local distribution. Federated averaging produces a dispatch engine that captures the union of all distributions without centralizing proprietary data.

---

🎯 Human-Readable Explanations (Explainable AI)

Every dispatch decision includes a structured, human-readable explanation:

╔══════════════════════════════════════════════════╗
║  🐡 Dispatch Decision                            ║
╠══════════════════════════════════════════════════╣
║                                                  ║
║  Selected:     gpt-oss-120b                      ║
║  Confidence:   87.3%                             ║
║  Latency:      4.2ms                             ║
║  Strategy:     modernbert-classifier             ║
║                                                  ║
║  Why:  This prompt involves code generation.     ║
║        gpt-oss-120b is optimized for code tasks  ║
║        and produces better results at lower cost ║
║        than alternatives.                        ║
║                                                  ║
║  Alternatives considered:                        ║
║    • hermes-3-405b  (18.2%) — better for writing║
║    • lfm-2.5-1.2b   (17.8%) — faster response   ║
║    • nemotron-3-ultra (16.5%) — more capable    ║
║                                                  ║
║  [Override] ← Users can change the decision     ║
╚══════════════════════════════════════════════════╝

Why This Matters for Trust

• ✅ Transparent: Users know why a model was chosen
• ✅ Overridable: Users can correct wrong decisions
• ✅ Self-improving: Overrides become training data
• ✅ Auditable: Every decision is logged with full context
• ✅ Compliant: Meets explainability requirements for regulated industries (GDPR "right to explanation")

---

🖼️ System Architecture Diagram

!Architecture Diagram

The system operates in two tiers with a continuous feedback loop:

• Tier 1 (Intelligent Dispatch Engine): Fast single-model selection using ModernBERT classifier, similarity search, cost optimization, and CMA-ES evolved weights. Confidence-based escalation to Tier 2.
• Tier 2 (Multi-Agent Orchestrator): Complex task decomposition, specialist agent dispatch, and GRPO reinforcement learning from outcomes.
• Feedback Loop: User feedback continuously improves both tiers via retraining and re-evolution.

---

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo wahidmounir/fugusashi-v1.3-Q8_0-GGUF --hf-file fugusashi-v1.3-Q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo wahidmounir/fugusashi-v1.3-Q8_0-GGUF --hf-file fugusashi-v1.3-Q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo wahidmounir/fugusashi-v1.3-Q8_0-GGUF --hf-file fugusashi-v1.3-Q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo wahidmounir/fugusashi-v1.3-Q8_0-GGUF --hf-file fugusashi-v1.3-Q8_0.gguf -c 2048

🚀 Quick Start Example

import fugusashi
# Option 1: Using the Python package
result = fugusashi.route(
    prompt="Write a Python function to calculate fibonacci numbers",
    models=["gpt-oss-120b", "hermes-3-405b", "lfm-2.5-1.2b"],
    optimize="cost-performance"  # or "quality", "latency", "balanced"
)
print(result.selected_model)    # e.g., "gpt-oss-120b"
print(result.confidence)        # e.g., 0.87
print(result.explanation)       # Natural language explanation
print(result.response)          # Model's response
# Option 2: OpenAI-compatible API
# POST http://localhost:8000/v1/chat/completions
# {
#   "model": "auto",
#   "messages": [{"role": "user", "content": "Your prompt here"}]
# }
# Response includes: routing_decision, confidence, explanation, model_used

CLI Usage

# Train the dispatch classifier on new data
fugusashi expand-data --input new_outcomes.jsonl
fugusashi train --model-dir .fugusashi_data/dispatch_model
# Benchmark the dispatch engine
fugusashi benchmark --model-dir .fugusashi_data/dispatch_model --held-out 30
# Run the intelligent dispatch engine
fugusashi serve --host 0.0.0.0 --port 8000

---

💡 Why Choose Fugusashi Over Alternatives?

| Feature | Fugusashi | Sakana Fugu | RouteLLM | LLMRouter |

|---------|-----------|-------------|----------|-----------|

| Dispatch Learning | ModernBERT (trained) | 7B coordinator LLM | Linear classifier | Multiple heuristics |

| Open Source | ✅ MIT | ❌ Proprietary | ✅ | ✅ |

| Federated | ✅ w/ privacy | ❌ | ❌ | ❌ |

| Explainability | ✅ Full NL explanations | ❌ | ❌ | ❌ |

| Overridable | ✅ User feedback loop | ❌ | ❌ | ❌ |

| CPU Inference | ✅ 83ms | ❌ GPU required | ✅ | ✅ |

| Model Classes | 3+ (expandable) | Proprietary | 2 tiers | 16 strategies |

| Cost | Free (MIT) | Proprietary pricing | Free | Free |

---

📊 Comparison with Sakana AI Fugu

Fugu uses a propreitary 7B-parameter coordinator LLM trained on undisclosed data. Fugusashi achieves competitive or better accuracy with:

• A 149M-parameter ModernBERT classifier (10× smaller)
• Fully open-source training data (224 examples)
• Federated learning capability
• Human-readable explanations
• Zero cost (MIT license, free OpenRouter models)

---

🔬 Research & Reproducibility

All results are fully reproducible:

| Resource | Link |

|----------|------|

| Source Code | github.com/eulogik/fugusashi |

| Trained Model Weights | huggingface.co/eulogik/fugusashi-model |

| Preference Dataset | huggingface.co/datasets/eulogik/fugusashi-dataset |

| Live Demo | huggingface.co/spaces/eulogik/fugusashi |

| Paper (arXiv) | github.com/eulogik/fugusashi/blob/main/paper/main.tex |

| API Documentation | eulogik.com/fugusashi |

| Training Data Format | See src/fugusashi/dataset.py |

---

📦 System Requirements

| Resource | Minimum | Recommended |

|----------|---------|-------------|

| RAM | 500MB | 2GB |

| CPU | Any x86_64 | Apple M-series / Intel i7+ |

| GPU | Not required | Optional (for fine-tuning) |

| Disk | 600MB (model) | 1GB (dataset + logs) |

| Python | 3.12+ | 3.13 |

| OS | Linux, macOS, Windows | Linux (production) |

---

🏷️ Install & Deploy

# Install via Pip
pip install fugusashi
# Or from source
git clone https://github.com/eulogik/fugusashi.git
cd fugusashi
pip install -e .
# Initialize with seed data
fugusashi expand-data
# Train the intelligent dispatch classifier
fugusashi train --model-dir .fugusashi_data/dispatch_model
# Serve with intelligent dispatch
fugusashi serve --host 0.0.0.0 --port 8000

---

🤝 Contributing

We welcome contributions! Fugusashi is a community-driven project.

• Training data: Submit prompt-model pairs for new model classes
• Model support: Add new dispatch strategies
• Federated nodes: Register new organizations
• Documentation: Improve guides and tutorials
• Bug reports: Help us improve reliability

See CONTRIBUTING.md for details.

---

📜 License

MIT License — see LICENSE for details.

---

📄 Citation

If you use Fugusashi in your research or production system:

@software{fugusashi2026,
  author = {Gautam Kishore},
  title = {Fugusashi: Federated Learned LLM Model Dispatch with Interpretable Decisions},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/eulogik/fugusashi},
  version = {1.3.0}
}

---

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