| 🚀 Building Deterministic AI Workflows: Fine-Tuning Qwen 2.5 I’m excited to share my project for the Hugging Face hashtag#build-small-hackathon! My goal was to explore how we can make agentic task planning more predictable and repeatable. One of the biggest challenges in agentic workflows is consistency. For this hackathon, I focused on fine-tuning a Qwen 2.5 (0.5B) model to see if it could produce the exact same set of predicted tasks every time it’s asked the same question. Key Project Highlights: Custom Dataset: Built a unique dataset from scratch with 1,000 training rows and 200 test rows. Fine-Tuning: Leveraged Azure Machine Learning and LoRA configuration to train the model efficiently using the SFTTrainer. Deployment: Pushed the final model to the Hugging Face Hub and created a Gradio-based web application to test the planner in real-time. The results are promising! Both local testing and the Hugging Face Space show the model providing consistent, deterministic outputs for task planning. Here is the youtube video: https://www.youtube.com/watch?v=Xfw3L1O0E6Q&feature=youtu.be Check out the project here: 📺 Full Technical Walkthrough: Fine-Tuning Qwen 2.5 for Agentic AI Task Planning 🌐 Live Demo on Hugging Face Spaces: https://huggingface.co/spaces/build-small-hackathon/agentflow It was a fantastic experience learning and contributing to the build-small-hackathon. hashtag#AI hashtag#MachineLearning hashtag#LLM hashtag#FineTuning hashtag#AgenticAI hashtag#HuggingFace hashtag#Qwen2 hashtag#BuildSmallHackathon hashtag#AzureML |
