What is MoM Model Family?

The MoM (Mixture of Models) Model Family is a curated collection of specialized, lightweight models designed for intelligent routing, content safety, and semantic understanding. These models power the core capabilities of Semantic Router, enabling fast, accurate, and privacy-preserving AI operations.

Overview

The MoM family consists of purpose-built models that handle specific tasks in the routing pipeline:

• Classification Models: Domain detection, PII identification, jailbreak detection
• Embedding Models: Semantic similarity, caching, retrieval
• Safety Models: Hallucination detection, content moderation
• Feedback Models: User intent understanding, conversation analysis

All MoM models are:

• Lightweight: 33M-600M parameters for fast inference
• Specialized: Fine-tuned for specific routing tasks
• Efficient: Many use LoRA adapters for minimal memory footprint
• Open Source: Available on HuggingFace for transparency and customization

Model Categories

1. Classification Models

Domain/Intent Classifier

• Model ID: models/mom-domain-classifier
• HuggingFace: LLM-Semantic-Router/lora_intent_classifier_bert-base-uncased_model
• Purpose: Classify user queries into 14 MMLU categories (math, science, history, etc.)
• Architecture: BERT-base (110M) + LoRA adapters
• Use Case: Route queries to domain-specific models or experts

PII Detector

• Model ID: models/mom-pii-classifier
• HuggingFace: LLM-Semantic-Router/lora_pii_detector_bert-base-uncased_model
• Purpose: Detect 35 types of personally identifiable information
• Architecture: BERT-base (110M) + LoRA adapters
• Use Case: Privacy protection, compliance, data masking

Jailbreak Detector

• Model ID: models/mom-jailbreak-classifier
• HuggingFace: LLM-Semantic-Router/lora_jailbreak_classifier_bert-base-uncased_model
• Purpose: Detect prompt injection and jailbreak attempts
• Architecture: BERT-base (110M) + LoRA adapters
• Use Case: Content safety, prompt security

Feedback Detector

• Model ID: models/mom-feedback-detector
• HuggingFace: llm-semantic-router/feedback-detector
• Purpose: Classify user feedback into 4 types (satisfied, need clarification, wrong answer, want different)
• Architecture: ModernBERT-base (149M)
• Use Case: Adaptive routing, conversation improvement

2. Embedding Models

Embedding Pro (High Quality)

• Model ID: models/mom-embedding-pro
• HuggingFace: Qwen/Qwen3-Embedding-0.6B
• Purpose: High-quality embeddings with 32K context support
• Architecture: Qwen3 (600M parameters)
• Embedding Dimension: 1024
• Use Case: Long-context semantic search, high-accuracy caching

Embedding Flash (Balanced)

• Model ID: models/mom-embedding-flash
• HuggingFace: google/embeddinggemma-300m
• Purpose: Fast embeddings with Matryoshka support
• Architecture: Gemma (300M parameters)
• Embedding Dimension: 768 (supports 512/256/128 via Matryoshka)
• Use Case: Balanced speed/quality, multilingual support

Embedding Light (Fast)

• Model ID: models/mom-embedding-light
• HuggingFace: sentence-transformers/all-MiniLM-L12-v2
• Purpose: Lightweight semantic similarity
• Architecture: MiniLM (33M parameters)
• Embedding Dimension: 384
• Use Case: Fast semantic caching, low-latency retrieval

3. Hallucination Detection Models

Halugate Sentinel

• Model ID: models/mom-halugate-sentinel
• HuggingFace: LLM-Semantic-Router/halugate-sentinel
• Purpose: First-stage hallucination screening
• Architecture: BERT-base (110M)
• Use Case: Fast hallucination detection, pre-filtering

Halugate Detector

• Model ID: models/mom-halugate-detector
• HuggingFace: KRLabsOrg/lettucedect-base-modernbert-en-v1
• Purpose: Accurate hallucination verification
• Architecture: ModernBERT-base (149M)
• Context Length: 8192 tokens
• Use Case: Factual accuracy verification, grounding check

Halugate Explainer

• Model ID: models/mom-halugate-explainer
• HuggingFace: tasksource/ModernBERT-base-nli
• Purpose: Explain hallucination reasoning via NLI
• Architecture: ModernBERT-base (149M)
• Classes: 3 (entailment/neutral/contradiction)
• Use Case: Explainable AI, hallucination analysis

Model Selection Guide

By Use Case

Use Case	Recommended Model	Why
Domain routing	mom-domain-classifier	14 MMLU categories, LoRA efficient
Privacy protection	mom-pii-classifier	35 PII types, token-level detection
Content safety	mom-jailbreak-classifier	Prompt injection detection
Semantic caching	mom-embedding-light	Fast, 384-dim, low latency
Long-context search	mom-embedding-pro	32K context, 1024-dim
Hallucination check	mom-halugate-detector	ModernBERT, 8K context
User feedback	mom-feedback-detector	4 feedback types, ModernBERT

By Performance Requirements

Requirement	Model Tier	Examples
Ultra-fast (<10ms)	Light	mom-embedding-light, mom-jailbreak-classifier
Balanced (10-50ms)	Flash	mom-embedding-flash, mom-domain-classifier
High-quality (50-200ms)	Pro	mom-embedding-pro, mom-halugate-detector

Configuration

Using MoM Models in Router

MoM models are pre-configured in router-defaults.yaml:

# Domain classification
classifier:
  category_model:
    model_id: "models/mom-domain-classifier"
    threshold: 0.6
    use_cpu: true

# PII detection
classifier:
  pii_model:
    model_id: "models/mom-pii-classifier"
    threshold: 0.9
    use_cpu: true

# Jailbreak protection
prompt_guard:
  model_id: "models/mom-jailbreak-classifier"
  threshold: 0.7
  use_cpu: true

Custom Model Registry

Override the default registry in your config.yaml:

mom_registry:
  "models/mom-domain-classifier": "your-org/custom-domain-classifier"
  "models/mom-pii-classifier": "your-org/custom-pii-detector"
  "models/mom-embedding-pro": "your-org/custom-embeddings"

Model Architecture

LoRA-Based Models

Many MoM models use LoRA (Low-Rank Adaptation) for efficiency:

• Base Model: BERT-base-uncased (110M parameters)
• LoRA Adapters: <1M parameters per task
• Memory Footprint: ~440MB base + ~4MB per adapter
• Inference Speed: Same as base model (~10-20ms on CPU)

ModernBERT Models

Newer models use ModernBERT for better performance:

• Architecture: ModernBERT-base (149M parameters)
• Context Length: 8192 tokens (vs 512 for BERT)
• Performance: Better accuracy on long-context tasks
• Use Cases: Hallucination detection, feedback classification

Next Steps

• Signal-Driven Decisions - Learn how MoM models power routing decisions
• Domain Routing - Use mom-domain-classifier for routing
• PII Detection - Configure mom-pii-classifier
• Semantic Cache - Use MoM embedding models