💻 programming

WorkflowLLM

Data-driven framework to enhance workflow orchestration capabilities for large language models

#automation
#Large language model
#Dataset
#fine-tuning
#Workflow orchestration
WorkflowLLM

Product Details

WorkflowLLM is a data-centric framework designed to enhance the capabilities of large language models (LLMs) in workflow orchestration. The core is WorkflowBench, a large-scale supervised fine-tuning dataset containing 106,763 samples from 1,503 APIs in 83 applications and 28 categories. WorkflowLLM created a WorkflowLlama model specifically optimized for workflow orchestration tasks by fine-tuning the Llama-3.1-8B model. Experimental results show that WorkflowLlama performs well in orchestrating complex workflows and generalizes well to unseen APIs.

Main Features

1
Data collection: Collect real-world Apple shortcuts from platforms like RoutineHub and transcribe them into Python-style code.
2
Query expansion: Use ChatGPT to generate diverse and complex task queries to enrich workflow datasets.
3
Workflow generation: Trained annotation models generate workflows for synthetic queries, which are then quality checked and merged with collected samples to form the final dataset.
4
Model fine-tuning: Use WorkflowBench to fine-tune the Llama-3.1-8B model and create WorkflowLlama, which is specifically optimized for workflow orchestration tasks.
5
Experimental results: WorkflowLlama performs well in orchestrating complex workflows and generalizing to unseen APIs.
6
Data preprocessing: Convert the original Apple Shortcuts plist format into an abstract syntax tree (AST) representation to improve the readability and practicality of the data.
7
Training and inference: Provide tools for model training and inference, supporting logging and saving intermediate checkpoints.

How to Use

1
1. Environment settings: Make sure Python 3.8 is installed and install all dependencies according to requirements.txt.
2
2. Data preprocessing: Run the preprocess/Convert_ShortCut_to_Python.py script to convert the .plist or .shortcut file into a Python compatible format.
3
3. Train the model: Execute the scripts/train.sh script to start training the model, providing the basic model path and data path as parameters.
4
4. Run inference: After the model training is completed, use scripts/infer.sh to run inference and provide the trained model checkpoint path.
5
5. View the results: Analyze the output of the model and evaluate the effect of workflow orchestration.
6
6. Fine-tuning and optimization: Fine-tune the model based on experimental results to adapt to specific workflow needs.

Target Users

WorkflowLLM is targeted at developers, data scientists and automation engineers, especially those who need to handle complex workflows and automation tasks. The framework enables these professionals to more efficiently build and deploy LLM-based solutions to automate business processes by providing large-scale data sets and optimized models.

Examples

Developers use WorkflowLLM to automate complex software development processes.

Data scientists utilize WorkflowLLM to process and analyze large-scale data sets.

Automation engineers use WorkflowLLM to design and optimize industrial automation processes.

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