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Version: Next 🚧

Intelligent LoRA Routing

This guide shows you how to combine intelligent routing (domain/embedding/keyword/MCP) with LoRA adapters to route requests to domain-specific models. LoRA routing uses the classification methods from previous guides to detect intent, then automatically selects the appropriate LoRA adapter on the vLLM backend.

Key Advantages​

  • Intent-aware adapter selection: Combines any classification method (domain/embedding/keyword/MCP) with LoRA adapters
  • Memory efficient: Share base model weights across multiple domain adapters (<1% parameters per adapter)
  • Transparent to users: Users send requests to one endpoint, router handles adapter selection
  • Flexible classification: Choose the best routing method for your use case (domain for accuracy, keyword for compliance, etc.)

What Problem Does It Solve?​

vLLM supports multiple LoRA adapters, but users must manually specify which adapter to use. LoRA routing automates this:

  • Manual adapter selection: Users don't know which adapter to use → Router classifies intent and selects adapter automatically
  • Memory efficiency: Multiple full models don't fit in GPU → LoRA adapters share base weights (~1% overhead per adapter)
  • Deployment simplicity: Managing multiple model endpoints is complex → Single vLLM instance serves all adapters
  • Intent detection: Generic base model lacks domain expertise → Router routes to specialized adapters based on query content

When to Use​

  • Multi-domain vLLM deployments with LoRA adapters for different domains (technical, medical, legal, etc.)
  • Automatic adapter selection where you want users to send requests without knowing adapter names
  • Combining classification + LoRA: Use domain routing for accuracy, keyword routing for compliance, or MCP for custom logic
  • Memory-constrained scenarios where multiple full models don't fit but LoRA adapters do
  • A/B testing different adapter versions by adjusting category scores

Configuration​

Prerequisites​

  • A running vLLM server with LoRA support enabled
  • LoRA adapter files (fine-tuned for specific domains)
  • Envoy + the router (see Installation guide)

1. Start vLLM with LoRA Adapters​

First, start your vLLM server with LoRA support enabled:

vllm serve meta-llama/Llama-2-7b-hf \
  --enable-lora \
  --lora-modules \
    technical-lora=/path/to/technical-adapter \
    medical-lora=/path/to/medical-adapter \
    legal-lora=/path/to/legal-adapter \
  --host 0.0.0.0 \
  --port 8000

Key flags:

  • --enable-lora: Enables LoRA adapter support
  • --lora-modules: Registers LoRA adapters with their names and paths
  • Format: adapter-name=/path/to/adapter

2. Router Configuration​

Put this in config/config.yaml (or merge into your existing config):

# Category classifier (required for intent detection)
classifier:
  category_model:
    model_id: "models/mom-domain-classifier"
    threshold: 0.6
    use_cpu: true
    category_mapping_path: "models/mom-domain-classifier/category_mapping.json"

# vLLM endpoint hosting your base model + LoRA adapters
vllm_endpoints:
  - name: "vllm-primary"
    address: "127.0.0.1"
    port: 8000
    weight: 1

# Define base model and available LoRA adapters
model_config:
  "llama2-7b":
    reasoning_family: "llama2"
    preferred_endpoints: ["vllm-primary"]
    # IMPORTANT: Define all available LoRA adapters here
    loras:
      - name: "technical-lora"
        description: "Optimized for programming and technical questions"
      - name: "medical-lora"
        description: "Specialized for medical and healthcare domain"
      - name: "legal-lora"
        description: "Fine-tuned for legal questions"

# Default model for fallback
default_model: "llama2-7b"

# Categories with LoRA routing
categories:
  - name: "technical"
    description: "Programming, software engineering, and technical questions"
    system_prompt: "You are an expert software engineer."
    model_scores:
      - model: "llama2-7b"           # Base model name
        lora_name: "technical-lora"  # LoRA adapter to use
        score: 1.0
        use_reasoning: true
        reasoning_effort: "medium"

  - name: "medical"
    description: "Medical and healthcare questions"
    system_prompt: "You are a medical expert."
    model_scores:
      - model: "llama2-7b"
        lora_name: "medical-lora"    # Different LoRA for medical
        score: 1.0
        use_reasoning: true
        reasoning_effort: "high"

  - name: "legal"
    description: "Legal questions and law-related topics"
    system_prompt: "You are a legal expert."
    model_scores:
      - model: "llama2-7b"
        lora_name: "legal-lora"      # Different LoRA for legal
        score: 1.0
        use_reasoning: true
        reasoning_effort: "high"

  - name: "general"
    description: "General questions"
    system_prompt: "You are a helpful assistant."
    model_scores:
      - model: "llama2-7b"           # No lora_name = uses base model
        score: 0.8
        use_reasoning: false

How It Works​

LoRA routing combines intelligent classification with vLLM's LoRA adapter support:

Flow:

  1. User sends query to router (doesn't specify adapter)
  2. Classification using any method (domain/embedding/keyword/MCP) detects intent
  3. Category matched (e.g., "technical" category)
  4. Router looks up model_scores for that category
  5. LoRA adapter selected via lora_name field (e.g., "technical-lora")
  6. Request forwarded to vLLM with model="technical-lora"
  7. vLLM loads adapter and generates response with domain-specific knowledge

Key insight: The classification method (domain/embedding/keyword/MCP) determines the category, then the category's lora_name determines which adapter to use.

Test Domain Aware LoRA Routing​

Send test queries and verify they're classified correctly:

# Technical query
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "MoM", "messages": [{"role": "user", "content": "Explain async/await in JavaScript"}]}'

# Medical query
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "MoM", "messages": [{"role": "user", "content": "What causes high blood pressure?"}]}'

Check the router logs to confirm the correct LoRA adapter is selected for each query.

Real-World Use Cases​

1. Healthcare Platform (Domain Routing + LoRA)​

Problem: Medical queries need specialized adapters, but users don't know which to use Solution: Domain routing classifies into diagnosis/pharmacy/mental-health, routes to corresponding LoRA adapters Impact: Automatic adapter selection, 70GB memory vs 210GB for 3 full models

2. Legal Tech (Keyword Routing + LoRA for Compliance)​

Problem: Compliance requires auditable routing to jurisdiction-specific legal adapters Solution: Keyword routing detects "US law"/"EU law"/"contract" keywords, routes to compliant LoRA adapters Impact: 100% auditable routing decisions, 95% citation accuracy with specialized adapters

3. Customer Support (Embedding Routing + LoRA)​

Problem: Support queries span IT/HR/finance, users phrase questions in many ways Solution: Embedding routing matches semantic intent, routes to department-specific LoRA adapters Impact: Handles paraphrases, single endpoint serves all departments with <10ms adapter switching

4. EdTech Platform (Domain Routing + LoRA)​

Problem: Students ask math/science/literature questions, need subject-specific tutors Solution: Domain routing classifies academic subject, routes to subject-specific LoRA adapters Impact: 4 specialized tutors for cost of 1.2 base models, 70% cost savings

5. Multi-Tenant SaaS (MCP Routing + LoRA)​

Problem: Each tenant has custom LoRA adapters, need dynamic routing based on tenant ID Solution: MCP routing queries tenant database, returns tenant-specific LoRA adapter name Impact: 1000+ tenants with custom adapters, private routing logic, A/B testing support

Next Steps​