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Workflowllm
Overview :
WorkflowLLM is a data-centric framework designed to enhance the orchestration capabilities of large language models (LLMs). At its core is WorkflowBench, a large-scale supervised fine-tuning dataset containing 106,763 samples from 1,503 APIs across 83 applications and 28 categories. WorkflowLLM fine-tunes the Llama-3.1-8B model to create the WorkflowLlama model optimized specifically for workflow orchestration tasks. Experimental results indicate that WorkflowLlama excels in orchestrating complex workflows and generalizes well to unseen APIs.
Target Users :
The target audience for WorkflowLLM includes developers, data scientists, and automation engineers, especially those who need to manage complex workflows and automate tasks. The framework empowers these professionals to effectively build and deploy LLM-based solutions for automating business processes by providing large-scale datasets and optimized models.
Use Cases
Developers use WorkflowLLM to automate complex software development processes.
Data scientists leverage WorkflowLLM to process and analyze large-scale datasets.
Automation engineers employ WorkflowLLM to design and optimize industrial automation processes.
Features
Data Collection: Gather real-world Apple Shortcuts from platforms like RoutineHub and transcribe them into Python-style code.
Query Expansion: Use ChatGPT to generate diverse and complex task queries to enrich the workflow dataset.
Workflow Generation: A trained annotation model generates workflows for synthetic queries, followed by quality checks and merging with collected samples to form the final dataset.
Model Fine-tuning: Use WorkflowBench to fine-tune the Llama-3.1-8B model, creating WorkflowLlama specifically optimized for workflow orchestration tasks.
Experimental Results: WorkflowLlama demonstrates excellent performance in orchestrating complex workflows and generalizing to unseen APIs.
Data Preprocessing: Convert raw Apple Shortcuts plist format into abstract syntax tree (AST) representation to enhance readability and utility.
Training and Inference: Provide tools for model training and inference, supporting logging and saving of intermediate checkpoints.
How to Use
1. Environment Setup: Ensure Python 3.8 is installed and install all dependencies as per requirements.txt.
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2. Data Preprocessing: Run the preprocess/Convert_ShortCut_to_Python.py script to convert .plist or .shortcut files into a Python-compatible format.
3. Model Training: Execute the scripts/train.sh script to begin training the model, providing the base model path and data path as parameters.
4. Run Inference: After model training is complete, use scripts/infer.sh to run inference, supplying the path to the trained model checkpoint.
5. Review Results: Analyze the model's output to evaluate the effectiveness of the workflow orchestration.
6. Fine-tuning and Optimization: Adjust the model based on experimental results to better fit specific workflow requirements.
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