#atom

Subtitle:

Open-source language models optimized for tool calling capabilities with strong performance at smaller parameter sizes

Core Idea:

Qwen models (particularly the Qwen 2.5 series) demonstrate exceptional function calling capabilities even at smaller parameter sizes (7B-14B), making them ideal for building locally-running agent applications that require tool interaction.

Key Principles:

1. Parameter Efficiency:
   • Strong function calling performance with relatively small model sizes (7B, 14B)
2. Open Source Availability:
   • Freely available for local deployment without API dependencies
3. Structured Output Quality:
   • Consistent generation of properly formatted function calls with appropriate parameters
4. Local Deployment Focus:
   • Optimized for running on consumer hardware with reasonable latency

Why It Matters:

• Local Agent Development:
  • Enables building sophisticated tool-using agents that run entirely on local hardware
• Privacy Preservation:
  • Allows tool-calling applications without sending data to external APIs
• Cost Efficiency:
  • Eliminates API costs for applications requiring frequent tool calls
• Accessibility:
  • Makes advanced agent capabilities available to developers without API access

How to Implement:

1. Install Local Inference Engine:
   • Set up Ollama or similar tool for local model deployment
2. Pull Appropriate Qwen Model:
   • ollama pull qwen2:14b-instruct for 14B parameter model
   • ollama pull qwen2:7b-instruct for 7B parameter model
3. Configure Tool Definitions:
   • Define tools with clear names, descriptions and parameter specifications
4. Implement Tool Calling Loop:
   • Use LangChain, LlamaIndex or custom code to implement the tool calling execution loop

Example:

• Scenario:
  • Running a multi-agent travel booking system locally
• Application:

import ollama

# Initialize with Qwen model
llm = ollama.Ollama(model="qwen2:14b-instruct")

# Create agent with tools
agent = create_react_agent(
    llm=llm,
    tools=[search_flights, book_flight, transfer_to_hotel_assistant]
)

# Run agent
agent.invoke({"input": "Find me flights to Tokyo next week"})

• Result:
  • Fully functional local agent that can search flights, book tickets, and transfer to other agents, all running on a laptop with ~45 second response times

Connections:

• Related Concepts:
  • Tool Calling in LLMs: The fundamental capability Qwen models excel at
  • Local LLM Agents: Implementation approach using Qwen models
• Broader Concepts:
  • Berkeley Function Calling Leaderboard: Ranks Qwen models highly among open-source options
  • Local AI Models: Broader category of locally deployable AI systems

References:

1. Primary Source:
   • Berkeley Function Calling Leaderboard evaluation results
2. Additional Resources:
   • Ollama documentation for deploying Qwen models
   • Qwen model documentation and GitHub repository

Tags:

#qwen #function-calling #open-source-llm #local-deployment #tool-calling #ollama

Connections:

Sources:

• From: LangChain - Fully local multi-agent systems with LangGraph