LMFlow is a comprehensive toolkit for fine-tuning, aligning, and deploying large foundation models. It provides a modular library with support for various efficient fine-tuning methods (LoRA, QLoRA, LISA), alignment techniques (DPO, RAFT), and inference backends (vLLM, SGLang). The architecture separates concerns into models, pipelines, optimizers, and utilities, with factory patterns for extensibility. Key features include conversation templating for multiple model families, custom optimizers, and distributed training integration. The toolkit is actively maintained with recent commits and addresses current research in model alignment and efficiency.
This repository is a curated list of papers, datasets, and code repositories for multi-turn interactions with large language models, accompanied by a static website that serves as a companion to a survey paper published in Transactions on Machine Learning Research (TMLR) 2026. The website includes sections on tasks (instruction following and conversational engagement), improvement methods, open challenges, and a searchable benchmark explorer. The repository also includes a script for processing survey data from Excel files and the README, though this script is not required to run the website.