Optimizing Function Calling Models: The Role of Dataset Size and LoRA Fine-tuning

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 4.3 Full and partial training datasets The Octopus model demonstrates exceptional performance with 1,000 data points sampled for each API during its training phase. However, for training a new set of functions, cost efficiency becomes a consideration, given the need to generate a training dataset. In our analysis, generating 1,000 data points for a single API incurs a cost of 0.0224 USD, representing the investment required to train an Octopus-0 model for one specific function. By evaluating the Octopus-0, Octopus-2, and Octopus-3 models, we discern that sampling only 100 data points for one API can still achieve an accuracy of 98.095%, as depicted in Figure (4). Therefore, for individuals seeking to train their own Octopus model using our framework, we recommend a dataset size ranging from 100 to 1,000 data points. 4.4 Full training and LoRA training LoRA plays a crucial role in our framework, particularly when integrating the Octopus model across multiple applications to ensure smooth computation. Instead of employing full models for each API set, we opt for diverse LoRA trainings tailored to the specific function setups of different apps. As Figure (4) illustrates, switching to Lora training results in a minor accuracy decrease. Nonetheless, the maintained high accuracy levels are sufficiently robust for production deployment. 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.