Instructions to use bhaiyasingh45/functiongemma-multiagent-router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bhaiyasingh45/functiongemma-multiagent-router with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bhaiyasingh45/functiongemma-multiagent-router")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bhaiyasingh45/functiongemma-multiagent-router")
model = AutoModelForCausalLM.from_pretrained("bhaiyasingh45/functiongemma-multiagent-router")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bhaiyasingh45/functiongemma-multiagent-router with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bhaiyasingh45/functiongemma-multiagent-router"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bhaiyasingh45/functiongemma-multiagent-router",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bhaiyasingh45/functiongemma-multiagent-router

SGLang

How to use bhaiyasingh45/functiongemma-multiagent-router with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bhaiyasingh45/functiongemma-multiagent-router" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bhaiyasingh45/functiongemma-multiagent-router",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bhaiyasingh45/functiongemma-multiagent-router" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bhaiyasingh45/functiongemma-multiagent-router",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bhaiyasingh45/functiongemma-multiagent-router with Docker Model Runner:
```
docker model run hf.co/bhaiyasingh45/functiongemma-multiagent-router
```

functiongemma-multiagent-router / README.md

Bhaiya Hari Narayan Singh

Add comprehensive model card with usage examples

8396463 verified 4 months ago

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	---
	language:
	- en
	license: gemma
	library_name: transformers
	tags:
	- function-calling
	- multi-agent
	- router
	- gemma
	- fine-tuned
	- customer-support
	base_model: google/functiongemma-270m-it
	datasets:
	- bhaiyahnsingh45/multiagent-router-finetuning
	metrics:
	- accuracy
	pipeline_tag: text-generation
	widget:
	- text: "My app keeps crashing when I upload large files"
	example_title: "Technical Issue"
	- text: "I need a refund for my subscription"
	example_title: "Billing Request"
	- text: "What integrations do you support?"
	example_title: "Product Info"
	---

	# Multi-Agent Router (Fine-tuned FunctionGemma 270M)

	<div align="center">
	<img src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.png" alt="Hugging Face" width="100"/>

	Intelligent routing model for multi-agent customer support systems

	[![License: Gemma](https://img.shields.io/badge/License-Gemma-blue.svg)](https://ai.google.dev/gemma/terms)
	[![Model: FunctionGemma](https://img.shields.io/badge/Model-FunctionGemma-orange.svg)](https://huggingface.co/google/functiongemma-270m-it)
	[![Dataset](https://img.shields.io/badge/Dataset-Available-green.svg)](https://huggingface.co/datasets/bhaiyahnsingh45/multiagent-router-finetuning)
	</div>

	## 📋 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

	```
	<start_function_call>call:technical_support_agent{issue_type:crash,priority:high}<end_function_call>
	```

	## 💡 Usage Examples

	### Example 1: Technical Issue
	```python
	query = "I'm getting a 500 error when calling the API"
	result = route_query(query)
	# Output: technical_support_agent(issue_type="api_error", priority="high")
	```

	### Example 2: Billing Request
	```python
	query = "I need a refund for my annual subscription"
	result = route_query(query)
	# Output: billing_agent(request_type="refund", urgency="medium")
	```

	### Example 3: Product Question
	```python
	query = "What integrations do you support for project management?"
	result = route_query(query)
	# Output: product_info_agent(query_type="integrations", category="project_management")
	```

	## 🔧 Advanced Usage: Parse Function Calls

	```python
	def parse_function_call(output: str) -> dict:
	"""Extract function name and arguments from model output"""

	pattern = r'<start_function_call>call:(\w+)\{([^}]+)\}<end_function_call>'
	match = re.search(pattern, output)

	if match:
	func_name = match.group(1)
	params_str = match.group(2)

	# Parse parameters
	params = {}
	param_pattern = r'(\w+):(?:<escape>(.*?)<escape>\|([^,{}]+))'
	for p_match in re.finditer(param_pattern, params_str):
	key = p_match.group(1)
	val = p_match.group(2) or p_match.group(3).strip()
	params[key] = val

	return {
	"agent": func_name,
	"parameters": params
	}

	return {"agent": "unknown", "parameters": {}}

	# Use it
	query = "I was charged twice this month"
	result = route_query(query)
	parsed = parse_function_call(result)
	print(parsed)
	# Output: {'agent': 'billing_agent', 'parameters': {'request_type': 'dispute', 'urgency': 'high'}}
	```

	## 🏗️ Integration Example

	```python
	class MultiAgentRouter:
	def __init__(self, model_name: str):
	self.tokenizer = AutoTokenizer.from_pretrained(model_name)
	self.model = AutoModelForCausalLM.from_pretrained(
	model_name,
	device_map="auto",
	torch_dtype="auto"
	)
	self.system_msg = "You are an intelligent routing agent..."

	def route(self, query: str) -> dict:
	"""Route query and return agent + parameters"""
	messages = [
	{"role": "developer", "content": self.system_msg},
	{"role": "user", "content": query}
	]

	inputs = self.tokenizer.apply_chat_template(
	messages,
	tools=AGENT_TOOLS,
	add_generation_prompt=True,
	return_dict=True,
	return_tensors="pt"
	)

	outputs = self.model.generate(
	**inputs.to(self.model.device),
	max_new_tokens=128,
	pad_token_id=self.tokenizer.eos_token_id
	)

	result = self.tokenizer.decode(
	outputs[0][len(inputs["input_ids"][0]):],
	skip_special_tokens=False
	)

	return parse_function_call(result)

	# Usage
	router = MultiAgentRouter("bhaiyahnsingh45/functiongemma-multiagent-router")
	routing = router.route("My payment failed but I don't know why")
	print(f"Route to: {routing['agent']}")
	print(f"Parameters: {routing['parameters']}")
	```

	## 📈 Evaluation

	The model was evaluated on a held-out test set of 23 queries:

	- Routing Accuracy: 82.6%
	- False Positive Rate: 17.4%
	- Average Inference Time: ~50ms on T4 GPU

	## ⚠️ Limitations

	1. Language: Currently supports English only
	2. Domain: Optimized for customer support; may need fine-tuning for other domains
	3. Agents: Limited to 3 agent types (can be extended with additional training)
	4. Context: Works best with single-turn queries; multi-turn conversations may need context handling
	5. Edge Cases: Ambiguous queries may require fallback logic

	## 🔮 Future Improvements

	- [ ] Add support for more languages
	- [ ] Expand to 5+ agent types (sales, feedback, onboarding)
	- [ ] Handle multi-turn conversations
	- [ ] Add confidence scores for routing decisions
	- [ ] Support for compound queries requiring multiple agents

	## 📝 Citation

	```bibtex
	@misc{functiongemma_multiagent_router,
	author = {Bhaiya Singh},
	title = {Multi-Agent Router: Fine-tuned FunctionGemma for Customer Support},
	year = {2025},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/bhaiyahnsingh45/functiongemma-multiagent-router}}
	}
	```

	## 📄 License

	This model inherits the [Gemma License](https://ai.google.dev/gemma/terms) from the base model.

	## 🙏 Acknowledgments

	- Base model: [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it)
	- Training framework: [Hugging Face TRL](https://github.com/huggingface/trl)
	- Dataset: [bhaiyahnsingh45/multiagent-router-finetuning](https://huggingface.co/datasets/bhaiyahnsingh45/multiagent-router-finetuning)

	## 📧 Contact

	For questions, issues, or collaboration opportunities:
	- Open an issue on the [model repository](https://huggingface.co/bhaiyahnsingh45/functiongemma-multiagent-router)
	- Dataset issues: [dataset repository](https://huggingface.co/datasets/bhaiyahnsingh45/multiagent-router-finetuning)

	---

	Built with ❤️ using FunctionGemma and Hugging Face Transformers