Summary of Tinyagent: Function Calling at the Edge, by Lutfi Eren Erdogan et al.
TinyAgent: Function Calling at the Edge
by Lutfi Eren Erdogan, Nicholas Lee, Siddharth Jha, Sehoon Kim, Ryan Tabrizi, Suhong Moon, Coleman Hooper, Gopala Anumanchipalli, Kurt Keutzer, Amir Gholami
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents TinyAgent, an end-to-end framework for training and deploying small language model agents that can integrate various tools and APIs to fulfill user queries through function calling at the edge. It enables accurate function calling for open-source models via the LLMCompiler framework and curates a high-quality dataset for fine-tuning two small language models, TinyAgent-1.1B and 7B. The authors introduce a novel tool retrieval method to reduce input prompt length and utilize quantization to accelerate inference speed. As a driving application, they demonstrate a local Siri-like system for Apple’s MacBook that can execute user commands through text or voice input. The results show that the models can achieve function-calling capabilities comparable to larger models like GPT-4-Turbo while being fully deployed at the edge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating small language models that can help devices, like a MacBook, understand and respond to user commands. This means you can talk or type to your computer and it will do what you ask! The researchers made these models smaller so they can run on devices without needing to send information to the cloud. They also created a special way for the models to find the right tools to use when fulfilling user requests. This technology could be used in many different devices, making them more useful and interactive. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Inference » Language model » Prompt » Quantization
