maaza-nlm-orchestrator-9.6m

70% tool-routing accuracy · 70ms average latency · 9.60M parameters

The fastest production-ready orchestrator under 20M parameters. The official routing brain for the MCPBodega ecosystem.

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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.

For the full taxonomy and research, see the Maaza Paper v0.7

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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.

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Benchmarks (verified December 2025)

Metric	Value	Notes
Tool selection accuracy	70%	Held-out evaluation (100 samples)
Valid structured output	88%	Parsable JSON output
Average inference latency	70ms	RTX 4080, fp16, batch=1
Parameters	9,600,000

Initial reports of 90% were aspirational targets. Verified held-out performance is 70% tool selection with 88% valid output.

Comparison

Model	Parameters	Tool Accuracy	Latency
maaza-nlm-orchestrator-9.6m	9.6M	70%	70ms
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.

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One-line deployment

mcpbodega deploy nano-orchestrator

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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"}}]

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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

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License

Apache 2.0

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Citation

@misc{cyclecore2025maaza-nlm,
  author       = {CycleCore},
  title        = {Maaza NLM Orchestrator 9.6M: 70% Tool-Routing at 70ms},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/cyclecore/maaza-nlm-orchestrator-9.6m}
}

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Built with MCPBodega · December 2025