dpo

#大语言模型#MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training Pipeline. 训练医疗大模型，实现了包括增量预训练(PT)、有监督微调(SFT)、RLHF、DPO、ORPO、GRPO。

Align Anything: Training All-modality Model with Feedback

A library with extensible implementations of DPO, KTO, PPO, ORPO, and other human-aware loss functions (HALOs).

#大语言模型#Easy and Efficient Finetuning LLMs. (Supported LLama, LLama2, LLama3, Qwen, Baichuan, GLM , Falcon) 大模型高效量化训练+部署.

#自然语言处理#tensorflow를 사용하여 텍스트 전처리부터, Topic Models, BERT, GPT, LLM과 같은 최신 모델의 다운스트림 태스크들을 정리한 Deep Learning NLP 저장소입니다.

#大语言模型#Official code of DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning

#大语言模型#Implementation for "Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs"

#大语言模型#🌾 OAT: A research-friendly framework for LLM online alignment, including preference learning, reinforcement learning, etc.

#大语言模型#An Efficient "Factory" to Build Multiple LoRA Adapters

#大语言模型#SiLLM simplifies the process of training and running Large Language Models (LLMs) on Apple Silicon by leveraging the MLX framework.

[CVPR 2025] Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

[ICLR 2025] IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation

#大语言模型#A RLHF Infrastructure for Vision-Language Models

Notus is a collection of fine-tuned LLMs using SFT, DPO, SFT+DPO, and/or any other RLHF techniques, while always keeping a data-first approach

Technical anaysis library for .NET

#大语言模型#This repository contains the code for SFT, RLHF, and DPO, designed for vision-based LLMs, including the LLaVA models and the LLaMA-3.2-vision models.

CodeUltraFeedback: aligning large language models to coding preferences

#大语言模型#[ICLR 2025] SuperCorrect: Advancing Small LLM Reasoning with Thought Template Distillation and Self-Correction

[ACL 2024] Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning

[NeurIPS 2024] Official code of $\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$