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A Survey of Direct Preference Optimization (DPO)
Official Implementation (Pytorch) of the "VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning", AAAI 2025
[ICML 2025] TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization
A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications
RankPO: Rank Preference Optimization
Notebooks to create an instruction following version of Microsoft's Phi 2 LLM with Supervised Fine Tuning and Direct Preference Optimization (DPO)
The Rap Music Generator project is an innovative LLM-based tool designed to create rap lyrics. It offers multiple fine-tuning approaches to accommodate diverse rap generation techniques, providing users with a versatile platform for generating unique and stylistically varied content.
Evaluate how LLaMA 3.1 8B handles paraphrased adversarial prompts targeting refusal behavior.
[CC 2025] [Official code] - Engaging preference optimization alignment in large language model for continual radiology report generation: A hybrid approach
Experiments, and how-to guide for the lecture "Large language models for Scientometrics"
Notebooks to create an instruction following version of Microsoft's Phi 1.5 LLM with Supervised Fine Tuning and Direct Preference Optimization (DPO)
[ICML 2025 Workshop FM4BS] AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
EPFLLaMA: A lightweight language model fine-tuned on EPFL curriculum content. Specialized for STEM education and multiple-choice question answering. Implements advanced techniques like SFT, DPO, and quantization.
Enhancing paraphrase-type generation using Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF), with large-scale HPC support. This project aligns model outputs to human-ranked data for robust, safety-focused NLP.
EPFLLaMA: A lightweight language model fine-tuned on EPFL curriculum content. Specialized for STEM education and multiple-choice question answering. Implements advanced techniques like SFT, DPO, and quantization.
This repository explores two key approaches to fine-tuning large language models — Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) — to align model behavior with human intent and task objectives.
Fine-tune a model that generates exams based on lecture materials.
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