**Intelligent routing model for multi-agent customer support systems**
[](https://ai.google.dev/gemma/terms)
[](https://huggingface.co/google/functiongemma-270m-it)
[](https://huggingface.co/datasets/bhaiyahnsingh45/multiagent-router-finetuning)
## 📋 Model Description
This model is a **fine-tuned version of Google's FunctionGemma 270M** specifically trained for intelligent routing in multi-agent customer support systems. It learns to:
1. **Classify user intent** from natural language queries
2. **Route to the appropriate specialist agent**
3. **Extract relevant parameters** (priority, urgency, category)
### 🤖 Supported Agents
The model routes queries to three specialized agents:
| Agent | Handles | Parameters |
|-------|---------|------------|
| 🔧 **Technical Support** | Crashes, bugs, API errors, authentication issues | `issue_type`, `priority` |
| 💰 **Billing** | Payments, refunds, subscriptions, invoices | `request_type`, `urgency` |
| 📊 **Product Info** | Features, integrations, plans, compliance | `query_type`, `category` |
## 🎯 Training Details
### Base Model
- **Model**: `google/functiongemma-270m-it`
- **Parameters**: 270 Million
- **Architecture**: Gemma with function calling capabilities
### Fine-tuning Configuration
- **Training Samples**: 92
- **Test Samples**: 23
- **Epochs**: 15
- **Batch Size**: 4
- **Learning Rate**: 5e-05
- **GPU**: NVIDIA T4 (Google Colab Free Tier)
- **Training Time**: ~5-8 minutes
### Dataset
Fine-tuned on [bhaiyahnsingh45/multiagent-router-finetuning](https://huggingface.co/datasets/bhaiyahnsingh45/multiagent-router-finetuning) containing 85 realistic customer support queries across three categories.
## 📊 Performance
| Metric | Before Training | After Training | Improvement |
|--------|----------------|----------------|-------------|
| **Accuracy** | 4.3% | 82.6% | **+78.3%** |
| **Correct Predictions** | 1/23 | 19/23 | +18 |
### Per-Agent Performance
- **Technical Support**: High accuracy on crash reports, API errors, authentication issues
- **Billing**: Excellent routing for refunds, payments, subscription management
- **Product Info**: Strong performance on feature queries, integrations, compliance questions
## 🚀 Quick Start
### Installation
```bash
pip install transformers torch
```
### Basic Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import re
import json
# Load model and tokenizer
model_name = "bhaiyahnsingh45/functiongemma-multiagent-router"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype="auto"
)
# Define your agent tools
from transformers.utils import get_json_schema
def technical_support_agent(issue_type: str, priority: str) -> str:
"""
Routes technical issues to specialized support team.
Args:
issue_type: Type of technical issue (crash, authentication, performance, api_error, etc.)
priority: Priority level (low, medium, high)
"""
return f"Routing to Technical Support: {issue_type} with {priority} priority"
def billing_agent(request_type: str, urgency: str) -> str:
"""
Routes billing and payment queries.
Args:
request_type: Type of request (refund, invoice, upgrade, cancellation, etc.)
urgency: How urgent (low, medium, high)
"""
return f"Routing to Billing: {request_type} with {urgency} urgency"
def product_info_agent(query_type: str, category: str) -> str:
"""
Routes product information queries.
Args:
query_type: Type of query (features, comparison, integrations, limits, etc.)
category: Category (plans, storage, mobile, security, etc.)
"""
return f"Routing to Product Info: {query_type} about {category}"
# Get tool schemas
AGENT_TOOLS = [
get_json_schema(technical_support_agent),
get_json_schema(billing_agent),
get_json_schema(product_info_agent)
]
# System message
SYSTEM_MSG = "You are an intelligent routing agent that directs customer queries to the appropriate specialized agent."
# Function to route queries
def route_query(user_query: str):
"""Route a user query to the appropriate agent"""
messages = [
{"role": "developer", "content": SYSTEM_MSG},
{"role": "user", "content": user_query}
]
# Format prompt
inputs = tokenizer.apply_chat_template(
messages,
tools=AGENT_TOOLS,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt"
)
# Generate
outputs = model.generate(
**inputs.to(model.device),
max_new_tokens=128,
pad_token_id=tokenizer.eos_token_id
)
# Decode
result = tokenizer.decode(
outputs[0][len(inputs["input_ids"][0]):],
skip_special_tokens=False
)
return result
# Example usage
query = "My app crashes when I try to upload large files"
result = route_query(query)
print(f"Query: {query}")
print(f"Routing: {result}")
```
### Expected Output Format
```