Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related works 2 Related works 3 Methodology and 3.1 Causal language model as a classification model 3 Methodology and 3.1 Causal language model as a classification model 3.2 Functional token 3.2 Functional token 3.3 Dataset collection 3.3 Dataset collection 3.4 Model development and training 3.4 Model development and training 4 Experiments and 4.1 Android function calls 4 Experiments and 4.1 Android function calls 4.2 Extension to Vehicle, Yelp, and DoorDash function sets 4.2 Extension to Vehicle, Yelp, and DoorDash function sets 4.3 Full and partial training datasets and 4.4 Full training and LoRA training 4.3 Full and partial training datasets and 4.4 Full training and LoRA training 4.5 Parallel and nested function call and 4.6 Weighted loss function for special tokens 4.5 Parallel and nested function call and 4.6 Weighted loss function for special tokens 5 Discussion and future works and References 5 Discussion and future works and References Appendix Appendix A.1 Android function examples A.1 Android function examples A.2 Vehicle function examples A.2 Vehicle function examples 5 Discussion and future works Our current training initiative proves that any specific function can be encapsulated into a newly coined term, functional token, a novel token type seamlessly integrated into both the tokenizer and the model. This model, through a cost-effective training process amounting to merely two cents, facilitates the deployment of AI agents characterized by their remarkably low latency and high accuracy. The potential impacts of our research are extensive. For application developers, including those at DoorDash and Yelp, our model paves the way for training on application-specific scenarios. Developers can pinpoint the APIs most utilized by their audience, transform these into functional tokens for the Octopus model, and proceed with deployment. This strategy has the capacity to fully automate app workflows, emulating functionalities akin to Apple’s Siri, albeit with significantly enhanced response speeds and accuracy. Furthermore, the model’s application within operating systems of PCs, smartphones, and wearable technology presents another exciting avenue. Software developers could train minor LoRAs specific to the operating system. By accumulating multiple LoRAs, the model facilitates efficient function calling across diverse system components. For instance, incorporating this model into the Android ecosystem would enable Yelp and DoorDash developers to train distinct LoRAs, thus rendering the model operational on mobile platforms as well. Looking ahead, we aim to develop a model dedicated to on-device reasoning. Our ambitions are dual-pronged: firstly, to achieve notable speed enhancements for cloud deployments, vastly outpacing GPT-4 in speed metrics. Secondly, to support local deployment, offering a valuable solution for users mindful of privacy or operational costs. This dual deployment strategy not only extends the model’s utility across cloud and local environments, but also caters to user preferences for either speed and efficiency or privacy and cost savings. 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This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors:
(1) Wei Chen, Stanford University, with equal contribution and a corresponding author {weichen6}@stanford.edu;
(2) Zhiyuan Li, Stanford University and a corresponding author {zhiyuan8}@stanford.edu. Authors: Authors: (1) Wei Chen, Stanford University, with equal contribution and a corresponding author {weichen6}@stanford.edu; (2) Zhiyuan Li, Stanford University and a corresponding author {zhiyuan8}@stanford.edu.

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