HomeRouter-S1 (olanigan/HomeRouter-S1)

Calibrated 120M System-1 Small Decision Model (SDM) for sub-26ms, zero-parsing-failure agentic tool intent routing and tier triage.

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⚡ Executive Summary

Modern AI agent frameworks (Antigravity, Claude Code, Cursor, LangChain) spend substantial compute budgets and latency classifying routing intents, triaging tool dispatches, or determining next actions.

HomeRouter-S1 is a non-autoregressive, calibrated Small Decision Model (SDM) fine-tuned on 20,582 multi-turn agent execution turns. Built upon ModernBERT and the LayaForDecision architecture, it classifies agent tool intents and tier triage in a single forward pass without autoregressive text decoding:

• Sub-26ms CPU Latency (ONNX) / Sub-43ms Apple Silicon Latency (Ollama Metal).
• Zero Output Tokens ($N_{\text{out}} = 0$): Evaluates entire decision candidate spaces in a single forward pass, bypassing generative decode loops entirely.
• 100% Strict Enum Guarantee: 0.00% schema parse errors, eliminating JSON formatting repair loops.
• Calibrated Confidence Gating: Fitted temperature ($T = 1.0618$) achieves $\text{ECE} \le 0.0363$. Applying a threshold of $\tau = 0.90$ escalates ambiguous prompts to secondary tiers while safely auto-dispatching high-confidence turns.
• Local Silicon Efficiency: Eliminates external network roundtrips and recurring per-token cloud inference costs.

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📊 Dual-Engine Silicon Benchmark

Performance telemetry across local host CPU and Apple Silicon Metal runtimes:

Model / Architecture	Mode / Runtime	TTFD (P50)	TTFD (P95)	Output Tokens ($N_{\text{out}}$)	Parse Error Rate	Calibrated ECE
Homestead Native ONNX (CPU)	Direct FP32 Forward Pass	25.69 ms	26.00 ms	0 tokens	0.00%	0.0363
Ollama MLX (Metal)	Native LayaForDecision	40.97 ms	42.50 ms	0 tokens	0.00%	0.0363

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🏷️ Supported Tool & Tier Catalog (10 Classes)

The model routes incoming agent dialogue turns across 10 discrete tools and meta-triage tiers:

0: bash           - Direct terminal command execution
1: edit_file      - Exact in-place text replacement
2: grep_search    - Codebase symbol & pattern search
3: read           - File inspection & document reading
4: run_command    - Workspace command & test runner execution
5: tier_cloud     - Cloud utility / mid-tier model routing
6: tier_frontier  - High-complexity escalation to Claude Sonnet / R1
7: tier_local     - On-device local SLM routing
8: view_file      - Read-only file preview
9: write          - Full file creation & overwriting

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🚀 Quickstarts

1. Python (transformers)

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "olanigan/HomeRouter-S1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

query = "Run the unit tests in packages/s1-train and check if they pass"
inputs = tokenizer(query, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# Calibrated temperature scaling
T = 1.0618
probs = torch.softmax(logits / T, dim=-1)
pred_idx = torch.argmax(probs, dim=-1).item()
confidence = probs[0, pred_idx].item()

action = model.config.id2label[pred_idx]
print(f"Predicted Action: {action} (Confidence: {confidence:.4f})")
print(f"Escalate to Frontier: {confidence < 0.90}")

2. Fast CPU Serving with ONNX Runtime

import numpy as np
import onnxruntime as ort
from transformers import AutoTokenizer

session = ort.InferenceSession("model.onnx")
tokenizer = AutoTokenizer.from_pretrained("olanigan/HomeRouter-S1")

inputs = tokenizer("cat src/index.ts", return_tensors="np")
ort_inputs = {
    "input_ids": inputs["input_ids"],
    "attention_mask": inputs["attention_mask"]
}
logits = session.run(None, ort_inputs)[0]
probs = np.exp(logits / 1.0618) / np.sum(np.exp(logits / 1.0618), axis=-1, keepdims=True)
print("Predicted tool index:", np.argmax(probs))

3. Local Silicon Execution with Ollama

# Pull or run directly once published, or build locally from the bundle:
ollama create olanigan/HomeRouter-S1 -f ollama_laya/Modelfile
ollama run olanigan/HomeRouter-S1 "find all *.ts files matching client"

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📦 Package Contents & File Sizes

File / Folder	Format / Target	Size	Description
model.safetensors	BF16 (PyTorch / Transformers)	239.5 MB	Standard weights for Python transformers & pipelines
model.onnx	FP32 (ONNX Runtime / CPU)	479.1 MB	Standalone zero-dependency graph for local CPU serving
ollama_laya/	FP32 Metal (Ollama MLX)	460.2 MB	33-tensor layout optimized for Apple Silicon Metal runtime
tokenizer/	Fast Tokenizers BPE	3.5 MB	ModernBERT fast tokenizer configuration & vocab
manifest.json	JSON	1.1 KB	Calibration parameters, ECE $\le 0.0363$, seed, and provenance
config.json	JSON	1.7 KB	Architecture specification and id2label mapping

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📜 Citation

@software{homerouter_s1_2026,
  author = {olanigan and Homestead Labs},
  title = {HomeRouter-S1: Calibrated 120M System-1 Small Decision Model for Agentic Tool Dispatch},
  year = {2026},
  url = {https://huggingface.co/olanigan/HomeRouter-S1}
}