Small Models, Big Teamwork

 

Copyright: Sanjay Basu

Why Multi-Agent Workflows Shine with Compact Powerhouses

In our previous discussion, we explored the rising significance of small models, particularly those in the 7B parameter range, and why they matter in AI systems. Now, let’s examine deeper into how these small models can form the backbone of sophisticated agentic workflows, where specialized agents, each powered by small models, can collaborate to achieve complex tasks.

Why the emphasis on multi-agent workflows? Think of it like building an all-star team: each small model is a specialist player with a clearly defined role, executing tasks swiftly and reliably. For instance, imagine MPT-7B rapidly extracting critical data, Falcon 7B handling nuanced summarization tasks, Llama 7B efficiently performing sentiment analysis, and Mistral 7B managing precision-based reasoning. This division of labor ensures every task is performed optimally without wasting computational resources. Here’s the kicker — our trusty NVIDIA A100 40GB GPUs are the perfect playground for these smaller open-source models. They comfortably run inference, allowing multiple specialized models to coexist on a single GPU, orchestrating complex workflows without breaking a sweat. With these nimble models, fine-tuning via parameter-efficient techniques like PEFT/LoRA remains viable, enabling you to adapt swiftly to evolving use cases without needing extensive hardware upgrades.

From finance and healthcare to automotive and customer support, multi-agent setups powered by small models enable powerful yet cost-effective AI solutions tailored to precise industry needs. It’s about leveraging the right model for the right task, rather than trying to force a generalist to fit every scenario. This targeted approach not only saves significant compute resources but also accelerates innovation and deployment. In a nutshell, smaller models empower smart, agile multi-agent workflows — making your A100 40GB investments shine and turning specialized AI from a luxury into an everyday reality.

What Are Agentic Workflows?

In agentic workflows, AI agents, each empowered by smaller, task-specific language models, work autonomously or semi-autonomously to execute specific functions. These agents can handle a variety of tasks like data extraction, decision-making, sentiment analysis, and more, often collaborating with other agents to achieve broader goals. The key here is specialization — small models focus on specialized, narrow domains, making them computationally efficient and fast.

Healthcare Use Case: Automated Diagnosis and Medical Report Generation

Copyright: Sanjay Basu

Agent Workflow in Healthcare:

1. Patient Data Extraction Agent: This agent retrieves relevant data from unstructured patient records, such as symptoms, previous diagnoses, and medical history. Powered by a model like FLAN-T5 (3B), this agent is focused on natural language understanding, identifying key phrases and medical terms.

2. Diagnostic Suggestion Agent: After parsing the data, this agent analyzes symptoms and suggests potential diagnoses based on current medical literature. A model like Mistral 7B is ideal for this task, thanks to its general language capabilities, which can combine various forms of input (like symptom lists, medical histories) to output meaningful suggestions.

3. Medical Report Generation Agent: Once the diagnostic agent suggests possible conditions, this agent takes over and generates a structured medical report. A model like Llama 7B or Falcon 7B, fine-tuned for medical document generation, could take the extracted data and diagnostic insights and produce a formatted, readable report for physicians.

4. Verification Agent: A secondary agent reviews the outputs, verifies that they align with the latest medical guidelines, and checks for any inconsistencies. A small model like MPT-7B could be used here, ensuring consistency and adherence to rules.

Why A100 GPUs Are Perfect for Agentic Workflows

The NVIDIA A100 40GB GPU is a natural choice for running these specialized, smaller models. Here’s why:

Memory Efficiency: Smaller models, like the 7B variants, fit comfortably within the 40GB memory capacity of the NVIDIA A100, allowing for efficient parallel processing and real-time inference. For tasks like medical report generation, these models need to process large chunks of text but don’t require the massive memory bandwidth demanded by larger models.

Throughput: The NVIDIA A100, on OCI AI infrastructure, excels at high-throughput operations, which is crucial for real-time systems. Healthcare workflows require fast decision-making, as time is often critical. The NVIDIA A100’s ability to handle high token-per-second throughput ensures that the agents can work quickly without bottlenecks.

Scalability and Flexibility: Even within the constraints of a single NVIDIA A100, these models can be optimized for multi-agent workflows, enabling high-performance, distributed tasks across different GPU resources. If more computational power is needed, scaling across multiple NVIDIA A100 GPUs in an efficient manner is straightforward.

Conclusion

By combining the power of small models with the robust performance of the NVIDIA A100 GPUs, we can create efficient, specialized agents in a variety of sectors, including healthcare. These agents work in tandem to provide fast, precise, and scalable solutions that make a real difference, especially in domains where accuracy and time are critical. With the flexibility of agentic workflows, we’re entering an era where AI’s specialization is its strength, and the A100 is here to support it every step of the way.

Appendix

Code for Workflow Orchestration

For a simple agent orchestration system, you can use a Python-based workflow orchestrator like Langchain or Haystack to manage these interactions. Here’s a simple Python-based example for workflow orchestration:

from langchain.agents import initialize_agent
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

# Initialize the models (simplified for illustration)
data_extraction_agent = OpenAI(model="text-davinci-003") # Placeholder for FLAN-T5 3B
diagnostic_agent = OpenAI(model="text-davinci-003") # Placeholder for Mistral 7B
report_generation_agent = OpenAI(model="text-davinci-003") # Placeholder for Llama 7B

# Define task-specific prompts
data_extraction_prompt = PromptTemplate("Extract key medical data from the following patient record: {record}")
diagnostic_prompt = PromptTemplate("Based on the symptoms {symptoms}, suggest potential diagnoses.")
report_generation_prompt = PromptTemplate("Generate a medical report based on the following data: {data}")

# Define workflow
def healthcare_workflow(patient_record):
# Step 1: Extract data
extracted_data = data_extraction_agent.run(data_extraction_prompt.format(record=patient_record))

# Step 2: Suggest diagnoses
diagnoses = diagnostic_agent.run(diagnostic_prompt.format(symptoms=extracted_data))

# Step 3: Generate medical report
medical_report = report_generation_agent.run(report_generation_prompt.format(data=diagnoses))

# Step 4: Return the report
return medical_report

# Example usage
patient_record = "Patient exhibits fever, coughing, and shortness of breath. History of asthma."
generated_report = healthcare_workflow(patient_record)
print(generated_report) 

Please let me know what you think!!