In recent years, language models have proven to be highly effective in various software applications, particularly in tasks related to automatic workflow. These models possess the crucial ability to call functions, which is essential for creating AI agents. However, while large-scale language models have shown high performance in cloud environments, they are often associated with concerns over privacy and cost. On the other hand, on-device models for function calling face challenges such as latency and accuracy issues. To address these challenges, our research introduces a new method that empowers an on-device model with 2 billion parameters to outperform GPT-4 in both accuracy and latency while reducing the context length by 95%. By comparing our method to Llama-7B with a RAG-based function calling mechanism, we were able to enhance latency by 35-fold. This significant improvement reduces latency levels to a point where deployment across various edge devices in production environments becomes feasible, meeting the performance requirements for real-world applications. Additionally, efforts are underway to deploy smaller-scale Large Language Models (LLMs) on edge devices like PCs and smartphones due to memory limitations and lower inference speeds. Open-source models of manageable sizes such as Gemma-2B, Gemma-7B, StableCode-3B, and Llama-7B have been introduced for this purpose. Research initiatives like Llama cpp and the MLC LLM framework enable the operation of 7B language models on mobile phones and other edge devices across different hardware platforms. Furthermore, advancements in function-calling capabilities of smaller-scale models have been observed through projects like NexusRaven, Toolformer, ToolAlpaca, Gorrilla, ToolLlama,and Taskmatrix. These projects demonstrate that 7B and 13B models can effectively call external APIs comparable to GPT-4 using a RAG-based method for function calling. Overall, the field of AI agents is rapidly evolving with advancements in AI assistant tools like MultiOn and Adept AI along with AI consumer products like Rabbit R1 and Humane AI Pin gaining traction. The use of language models in developing dependable software that empowers users through API calling and reasoning abilities showcases promising trends in the industry. Despite these advancements, concerns remain regarding privacy issues associated with reliance on cloud-based models along with inference costs and the need for constant Wi-Fi connectivity.
- Language models have proven effective in software applications, particularly in tasks related to automatic workflow
- Large-scale language models show high performance in cloud environments but raise concerns over privacy and cost
- On-device models for function calling face challenges such as latency and accuracy issues
- Research introduces a new method empowering on-device model with 2 billion parameters to outperform GPT-4 in accuracy and latency while reducing context length by 95%
- Efforts are underway to deploy smaller-scale Large Language Models (LLMs) on edge devices like PCs and smartphones due to memory limitations and lower inference speeds
- Open-source models of manageable sizes like Gemma-2B, Gemma-7B, StableCode-3B, and Llama-7B introduced for deployment on edge devices
- Advancements in function-calling capabilities of smaller-scale models observed through projects like NexusRaven, Toolformer, ToolAlpaca, Gorrilla, ToolLlama,and Taskmatrix
- Field of AI agents evolving rapidly with advancements in AI assistant tools like MultiOn and Adept AI along with AI consumer products like Rabbit R1 and Humane AI Pin gaining traction
Summary- Language models are like smart helpers in computer programs that can do tasks automatically.
- Big language models work really well in the internet cloud, but some people worry about privacy and cost.
- Small language models on devices have trouble being fast and accurate when they're asked to do things.
- A new way of making small device models with lots of information has made them better than a famous model called GPT-4 in doing things quickly and accurately.
- People are working on putting smaller smart models on everyday devices like computers and phones because they don't have a lot of memory and can be slow.
Definitions
- Language models: Smart helpers in computers that understand words and sentences to help with tasks.
- Parameters: Pieces of information used by the model to make decisions or predictions.
- Latency: The time it takes for something to happen after it's been asked for.
- Accuracy: How correct or precise something is.
- Inference speeds: How quickly the model can figure out answers or make decisions based on given information.
Introduction
In recent years, language models have become increasingly popular in various software applications due to their ability to perform automatic workflow tasks. These models are particularly useful for creating AI agents as they possess the crucial capability of calling functions. However, while large-scale language models have shown high performance in cloud environments, they also come with concerns over privacy and cost. On the other hand, on-device models face challenges such as latency and accuracy issues.
To address these challenges, a new research paper introduces a method that empowers an on-device model with 2 billion parameters to outperform GPT-4 in both accuracy and latency while reducing context length by 95%. This significant improvement makes it feasible to deploy these models across various edge devices in production environments, meeting the performance requirements for real-world applications.
Comparing Methods
The research paper compares its method to Llama-7B with a RAG-based function calling mechanism. The results show that their method enhances latency by 35-fold compared to Llama-7B. This improvement reduces latency levels to a point where deployment across different edge devices becomes possible without compromising performance.
Deployment of Smaller-Scale Models
Efforts are underway to deploy smaller-scale Large Language Models (LLMs) on edge devices like PCs and smartphones due to memory limitations and lower inference speeds. To achieve this goal, open-source models of manageable sizes such as Gemma-2B, Gemma-7B, StableCode-3B, and Llama-7B have been introduced.
Research initiatives like Llama cpp and the MLC LLM framework enable the operation of 7B language models on mobile phones and other edge devices across different hardware platforms. These advancements make it possible for smaller-scale models to effectively call external APIs comparable to GPT-4 using a RAG-based method for function calling.
Advancements in Function-calling Capabilities
Projects like NexusRaven, Toolformer, ToolAlpaca, Gorrilla, ToolLlama,and Taskmatrix have demonstrated advancements in function-calling capabilities of smaller-scale models. These projects showcase that 7B and 13B models can effectively call external APIs using a RAG-based method for function calling.
The Rapidly Evolving Field of AI Agents
The field of AI agents is rapidly evolving with advancements in AI assistant tools like MultiOn and Adept AI along with AI consumer products like Rabbit R1 and Humane AI Pin gaining traction. The use of language models in developing dependable software that empowers users through API calling and reasoning abilities showcases promising trends in the industry.
Concerns Over Privacy and Cost
Despite these advancements, concerns remain regarding privacy issues associated with reliance on cloud-based models along with inference costs and the need for constant Wi-Fi connectivity. As more companies rely on large-scale language models for their applications, there is a growing concern over user data privacy. Additionally, the cost of running these models on cloud platforms can be significant for businesses.
Conclusion
In conclusion, this research paper introduces a new method that addresses challenges faced by both large-scale and on-device language models. By empowering an on-device model with 2 billion parameters to outperform GPT-4 while reducing context length by 95%, this method makes it feasible to deploy these models across various edge devices without compromising performance. With ongoing efforts to deploy smaller-scale LLMs on edge devices and advancements in function-calling capabilities, the field of AI agents is rapidly evolving towards creating more efficient and dependable software solutions. However, concerns over privacy issues associated with cloud-based models highlight the need for further research in this area to ensure user data protection.
