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Octopus v2: On-device language model for super agent
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Language models have shown effectiveness in a variety of software applications, particularly in tasks related to automatic workflow. These models possess the crucial ability to call functions, which is essential in creating AI agents. Despite the high performance of large-scale language models in cloud environments, they are often associated with concerns over privacy and cost. Current on-device models for function calling face issues with latency and accuracy. Our research presents a new method that empowers an on-device model with 2 billion parameters to surpass the performance of GPT-4 in both accuracy and latency, and decrease the context length by 95\%. When compared to Llama-7B with a RAG-based function calling mechanism, our method enhances latency by 35-fold. This method reduces the latency to levels deemed suitable for deployment across a variety of edge devices in production environments, aligning with the performance requisites for real-world applications.
Forward citations
Cited by 8 Pith papers
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Software Engineering for and with GUI Agent
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An integrated mobile system for rice disease alerting that adds Gaussian noise to images for privacy, classifies with a CNN, and answers farmer questions with a fine-tuned GPT-2, but its privacy mechanism is not forma...
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OmniVLM: A Token-Compressed, Sub-Billion-Parameter Vision-Language Model for Efficient On-Device Inference
A sub-billion vision-language model using a fixed 9x reshape of visual tokens reports faster edge inference and higher benchmark scores than nanoLLAVA.
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When FinTech Meets Privacy: Securing Financial LLMs with Differential Private Fine-Tuning
DPFinLLM is a standard LoRA plus DP-SGD fine-tuning recipe applied to Llama2 and ChatGLM2 for financial sentiment; the experiments are mixed, generally below state-of-the-art, and key details are missing.
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