Llama 3 tool calling agents with Firefunction-v2
AI Summary
Summary: Fireworks Fire Function V2 Release
- Introduction
- Lance from Lang chain discusses Fireworks, a popular inference engine.
- New model release: Fire Function V2.
- Fire Function V2 excels at tool/function calling, competitive with GPD-40 but faster and cheaper.
- Tool Use
- Enhances model capabilities by connecting to external tools.
- Allows language models (LLMs) to interpret natural language and output necessary API/function calls.
- Fire Function V2 Features
- Built on Llama 3 Instruct, a strong base model.
- Aims to balance fine-tuning for function calling with preserving base model capabilities.
- Avoids overfitting to benchmarks, maintaining generalization to various tasks.
- Demonstration
- Installation and setup of Fire Function V2.
- Defining and binding a weather tool to the LLM.
- Example query: “What’s the weather like in San Francisco in Celsius?”
- LLM successfully outputs the correct function name and arguments.
- Real-World Example: Agent Evaluation
- Lang chain’s cookbook for agent evaluation.
- SQL database agent defined, using L graph to orchestrate.
- Agent architecture: LLM decides on tool use, invokes tool, and loops until a natural language response is returned.
- Testing and Results
- Fire Function V2 tested against a set of reference answers.
- Comparison with GPD-40 on a small agent evaluation challenge.
- Both models scored 60% on the test, with some questions answered correctly by one but not the other.
- Fire Function V2 shows promise for function calling and tool use in agent building.
- Conclusion
- Encourages testing Fire Function V2 in user-specific applications.
- Presents as a viable option for function calling needs.