Melusina-1.5B-JFP

🌊 MELUSINA — Jaro Flash Protocol Core v1

"Deterministic. Auditable. Structured. Always."

MELUSINA is a LoRA fine-tuned adapter on top of Qwen/Qwen2.5-1.5B, trained to operate under the Jaro Flash Protocol (JFP) v16E.0.0 — a deterministic AI governance framework designed for structured, auditable, and hallucination-resistant AI outputs.

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🧠 Model Details

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🎯 What is MELUSINA?

MELUSINA is the first public release of the JFP-Core engine — a fine-tuned language model that:

• Never confabulates — unknown facts return SIGNAL_UNKNOWN instead of hallucinated answers
• Produces structured JSON outputs — every response is machine-parseable and auditable
• Enforces constitutional rules — dangerous or unauthorized actions return SIGNAL_DENY
• Supports multi-agent orchestration — dispatches VIPER, VISION, EVOCOS, NEXUS, AORTANA agents
• Self-learning detection — recognizes recurring user patterns and suggests fine-tuning (LEARNING_SIGNAL_DETECTED)
• Full audit trail — every output includes audit_id and timestamp

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📊 Training Results

Final metrics:

• train_loss: 0.2238
• train_runtime: 12,850s (~3h 34min)
• train_samples_per_second: 0.117
• train_steps_per_second: 0.029

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🗂️ Dataset: JFP-Core-v1 v2 (1000 examples)

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🚀 Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base_model = "Qwen/Qwen2.5-1.5B"
adapter    = "jarohullowicki/Melusina-1.5B-JFP"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model     = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16)
model     = PeftModel.from_pretrained(model, adapter)
model.eval()

SYSTEM = (
    "You are JFP-Core-v1, a deterministic AI engine governed by Jaro Flash Protocol "
    "v16E.0.0. You never confabulate. Unknown = SIGNAL_UNKNOWN. "
    "Every output is structured and auditable."
)

messages = [
    {"role": "system",  "content": SYSTEM},
    {"role": "user",    "content": "What will the stock price of NVIDIA be in 2030?"},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=128, temperature=0.1)
print(tokenizer.decode(out[0], skip_special_tokens=True))
# Expected: {"status": "SIGNAL_UNKNOWN", "reason": "no_verified_data", "audit_id": "JFP-XXX", ...}

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🏗️ JFP Protocol — Signal Types

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🤖 JFP Agents

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⚠️ Limitations

• Based on Qwen2.5-1.5B — small model, best for structured/constrained tasks
• Optimized for JFP protocol outputs — not a general-purpose assistant
• English-primary training data
• Requires system prompt for proper JFP behavior

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📄 Citation

@misc{melusina2026,
  author    = {Kuchta, Jarosław Andrzej},
  title     = {MELUSINA: Jaro Flash Protocol Core v1 — Deterministic AI Engine},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/jarohullowicki/Melusina-1.5B-JFP}
}

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Built with ❤️ by Jaro | Jaro Flash Protocol v16E.0.0 | 2026