Small-Model Harness

Defensive harness for running 1B–12B parameter models in production agentic workflows.

Five-layer architecture: routing → validation → constraint → circuit break → context management. Delivered as a Hermes plugin (deterministic enforcement) + MCP server (analysis tools).

The Problem

Small models (1B–12B) have dramatically closed the quality gap with frontier models, but the reliability gap persists:

Failure Mode	Impact	Source
Doom Loops — repetitive death spirals under greedy sampling	Qwen3.5-4B: 22.9% loop rate	Antidoom (FTPO)
Context Rot — degradation with input length, not position	Effective window = ~1/3 of stated window	Chroma/NVIDIA
Format Drift — tool call fragility on long chains	87% accuracy vs GPT-4o's 92%	Qwen3-32B eval

Architecture

              ┌─────────────────────────────────────────┐
              │        ENTRY POINT / TASK DISPATCH      │
              └────────────────┬────────────────────────┘
                               │
              ┌────────────────▼────────────────────────┐
 LAYER 1      │         TASK CLASSIFIER                 │
 ROUTING      │  Classify complexity, route to model    │
              │  T1 (<4B) → T2 (4-8B) → T3 (9-12B) →   │
              │  T4 (cloud) cascade with confidence     │
              └────────────────┬────────────────────────┘
                               │
              ┌────────────────▼────────────────────────┐
 LAYER 2      │      PRE-CALL VALIDATION                │
 GUARDRAIL    │  Schema check, loop detection, budget   │
              │  Blocks bad calls BEFORE execution      │
              └────────────────┬────────────────────────┘
                               │
              ┌────────────────▼────────────────────────┐
 LAYER 3      │     CONSTRAINED DECODING ENGINE         │
 OUTPUT       │  XGrammar token masking (40μs)          │
 ENFORCEMENT  │  Guarantee valid JSON/tool call output  │
              │  (NOT YET IMPLEMENTED)                  │
              └────────────────┬────────────────────────┘
                               │
              ┌────────────────▼────────────────────────┐
 LAYER 4      │       CIRCUIT BREAKER                   │
 LOOP         │  3-state: closed → open → half-open     │
 DETECTION    │  Detect loops, break circuits, escalate  │
              └────────────────┬────────────────────────┘
                               │
              ┌────────────────▼────────────────────────┐
 LAYER 5      │      CONTEXT BUDGET                     │
 CONTEXT      │  Sliding window compaction              │
              │  1/3 effective window rule              │
              └─────────────────────────────────────────┘

Components

Component	What It Does	Status
Hermes Plugin	pre_tool_call hooks — schema validation, loop detection, circuit breaker, context budget, routing awareness	✅ Phase 1
MCP Server	5 tools: context status, compaction, task classification, routing, reset	✅ Phase 2+3
Task Classifier	Rule-based complexity scoring and model tier assignment	✅ Phase 3
Context Router	Tier cascade (T1→T2→T3→T4) with failure-based escalation	✅ Phase 3
Output Enforcement	Constrained decoding (XGrammar/Outlines) for guaranteed valid output	⏳ Planned
Output Verifier	Post-generation validation of tool call correctness	📋 Planned

Installation

As a Hermes Plugin

# Copy plugin to Hermes plugins directory
cp -r hermes-plugin ~/.hermes/profiles/dev/plugins/small-model-harness

# Enable it
hermes plugins enable small-model-harness

As an MCP Server

Add to ~/.hermes/config.yaml or ~/.hermes/profiles/dev/config.yaml:

mcp_servers:
  small-model-harness:
    command: python3
    args: ["/path/to/mcp-server/server.py"]
    enabled: true

MCP Server Tools

Tool	Description
harness_context_status	Query context budget utilization for a session
harness_compact	Compact session context (sliding window summarization)
harness_classify_task	Classify task complexity and suggest model tier
harness_route	Route task to model tier with cascade logic
harness_reset	Reset all harness state for a session

Tier Reference

Tier	Model Size	Example Models	Use Case
T1	<4B	SmolLM3, phi-4-mini	Simple extraction, classification
T2	4-8B	Qwen3-8B, Llama 3.2-8B	Single tool calls, basic routing
T3	9-12B	Ornith-1.0-9B, Qwen3-30B-A3B	Multi-step, reasoning, planning
T4	Cloud	DeepSeek V4, GPT-4o	Complex chains, security-critical

Development

# Setup
uv sync --group dev

# Run tests
uv run pytest tests/ -v

# Test MCP server
echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2026-07-28","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}' | python3 mcp-server/server.py

License

MIT