maaza-nlm-orchestrator-9.6m

95% tool accuracy (in-distribution) · 35ms latency · 9.60M parameters

The fastest raw orchestrator ever shipped under 20M parameters. The official routing brain for the MCPBodega ecosystem.

---

Performance (v1.0 — December 2025)

Evaluation Set	Accuracy	Notes
In-distribution	95%	Standard phrasing
Novel paraphrases	65%	Reasonable rewording
Adversarial (typos/slang)	25%	Extreme cases
Valid JSON	99.7%	Always parsable
Latency	35ms	RTX 4080 · fp16 · batch=1

With production wrapper (spell-check + one retry, +<60ms): → 92–94% end-to-end success at <110ms average (still phone-capable)

This is exactly how Replicate, LangGraph, Dust.tt, and every serious edge stack ships <20M routers in 2025.

Raw model is public and pure. Production deployments use the wrapper.

---

Paper

Task-Specialized Micro Language Models Outperform Larger Zero-Shot Models on Structured Data Extraction

Authors: CycleCore Technologies Date: November 22, 2025 Version: 0.7

Full Paper (PDF)

---

NLM Taxonomy (CycleCore, 2025)

Category	Parameters	Typical Capability
NLM	<10M	Routing, classification, orchestration
MLM	10–250M	Structured extraction
SLM	250M–1.5B	Reliable reasoning + extraction
LLM	>1.5B	General-purpose reasoning

maaza-nlm-orchestrator-9.6m is the current flagship of the NLM category.

---

Model Card

Metric	Value
Parameters	9,600,000
Architecture	7-layer Transformer decoder, SwiGLU, RoPE
Hidden size / Heads	320 / 8
Vocabulary	8,000 (BPE, tool-aware)
Context length	512 tokens

Trained exclusively on 36 real, production-ready MCP tools from MCPBodega (Doom, Puppeteer, code execution, file I/O, database queries, etc.). No synthetic or placeholder tools.

---

Comparison

Model	Parameters	Tool Accuracy	Latency
maaza-nlm-orchestrator-9.6m	9.6M	95%	35ms
NVIDIA Orchestrator-8B	8B	78%	≥800ms
Gorilla-7B	7B	52–58%	1–3s
ToolLlama-7B	7B	48–55%	2–4s

Ranks #1 under 20M parameters on latency-adjusted tool routing.

---

One-line deployment

mcpbodega deploy nano-orchestrator

---

Usage Example (PyTorch)

from model import MaazaNanoModel, MaazaNanoConfig
from tokenizer import BPETokenizer
import torch, json

tokenizer = BPETokenizer.load("tokenizer.json")
config = MaazaNanoConfig(**json.load(open("config.json")))
model = MaazaNanoModel(config)
model.load_state_dict(torch.load("model.pt", weights_only=True))
model.eval().cuda()

prompt = "<|user|>search for cats on the internet<|assistant|>"
input_ids = torch.tensor([tokenizer.encode(prompt)]).cuda()

with torch.no_grad():
    for _ in range(64):
        logits = model(input_ids)["logits"]
        next_token = logits[0, -1].argmax(-1)
        input_ids = torch.cat([input_ids, next_token[None, None]], dim=-1)
        if next_token.item() in tokenizer.special_tokens.values():
            break

print(tokenizer.decode(input_ids[0].tolist()))
# → [{"tool": "web_search", "params": {"query": "cats"}}]

---

Production Wrapper (92–94% end-to-end)

For production deployments, use the included production_router.py which adds spell-correction and retry logic:

from production_router import route_with_retry

result = route_with_retry("serch for cats on teh interent", model, tokenizer)
# Handles typos, retries on invalid JSON → 92-94% success rate

---

Supported Tools (36)

Tool	Description
web_search	Search the web
web_fetch	Fetch URL content
file_read	Read local files
file_write	Write local files
code_execute_python	Run Python code
code_execute_bash	Run shell commands
code_execute_js	Run JavaScript
email_send	Send emails
slack_send	Send Slack messages
calendar_add	Create calendar events
database_query	Query databases
puppeteer_navigate	Browser navigation
puppeteer_click	Browser clicks
puppeteer_screenshot	Take screenshots
doom_mcp	Play Doom
bitchat_send	BLE mesh chat
voice_mcp	Text-to-speech
maaza_extract_json	Extract structured data
json_validate	Validate JSON
csv_parse	Parse CSV files
regex_match	Pattern matching
calculator	Math operations
weather_lookup	Weather data
crypto_lookup	Crypto prices
stock_lookup	Stock prices
news_fetch	News headlines
mcpbodega_chat	MCPBodega chat rooms
mcpbodega_deploy	Deploy MCPs
mcpbodega_list	List MCPs
github_issue	Create GitHub issues
scratchpad_mcp	Temporary storage
health_check	Service health checks
cyclecore_terminal	Terminal commands
image_caption	Image descriptions
slmbench_query	Benchmark queries
translator	Translation

---

License

Apache 2.0

---

Citation

@misc{cyclecore2025maaza-nlm,
  author       = {CycleCore Technologies},
  title        = {Task-Specialized Micro Language Models Outperform Larger Zero-Shot Models on Structured Data Extraction},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/CycleCoreTechnologies/maaza-nlm-orchestrator-9.6m}
}

---

CycleCore Technologies · @CycleCoreTech

cyclecore.ai · mcpbodega.com · slmbench.com

December 2025