LLM Fine-Tuning and Evaluation Solution Accelerator

**LLM Fine-Tuning and Evaluation with Databricks and SuperAnnotate** Instruction fine-tuning for large language models (LLMs) opens the door to a wide variety of applications, each tailored to specific needs and use cases. No matter your objective, the one constant is the need for high-quality, carefully curated datasets. SuperAnnotate is a comprehensive platform that streamlines dataset creation, project management, and model evaluation, making it an ideal tool for large-scale data projects. With a fully customizable interface, seamless model integration, and advanced analytics, it simplifies each step of the AI pipeline while maintaining top-quality standards. - **Data Creation**: Natively multimodal and fully customizable annotation editor that you can tailor to any task, from SFT to RAG, Agents, and much more. - **Project Management:** Real-time analytics and insights to monitor project progress and team performance, perfect for large-scale data creation or model evaluation. - **Model Evaluation:** Go beyond standard metrics by creating custom evaluation tasks, connecting directly to your tools, and involving your experts in live testing. **Dataset** When building a dataset for maths refusal we must create and/or curate a set of prompt-response pairs where the model consistently refuses to answer questions involving mathematical calculations or computations. For this example, we have curated a dataset of around 500 prompt and response pairs stored in databricks that you can use, but if you are fine-tuning for another use-case, you can follow the same steps as below to create your initial IFT dataset or import an existing one. To preview the dataset you can follow the instructions in the code block in the cell below. **Content** 1. Authentication 2. Project Creation 3. UI Builder 4. Data Import 5. Review and edit dataset 6. Fine-Tune LLM in Databricks 7. Model Serving 8. Model evaluation in SuperAnnotate

搜集汇总

背景与挑战

背景概述

该资源是一个关于使用Databricks和SuperAnnotate平台进行大语言模型（LLM）微调与评估的解决方案指南。它概述了从数据创建、项目管理到模型评估的全流程，并提供了一个包含约500个提示-响应对的数学拒绝场景示例数据集以供参考。