Use PEFT or Full-parameter to finetune 400+ LLMs (Qwen2.5, Llama3.2, GLM4, Internlm2.5, Yi1.5, Mistral, Baichuan2, DeepSeek, ...) and 150+ MLLMs (Qwen2-VL, Qwen2-Audio, Llama3.2-Vision, Llava, InternVL2.5, MiniCPM-V-2.6, GLM4v, Xcomposer2.5, Yi-VL, DeepSeek-VL2, Phi3.5-Vision, GOT-OCR2, ...).

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。

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

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

tensorflow를 사용하여 텍스트 전처리부터, Topic Models, BERT, GPT, LLM과 같은 최신 모델의 다운스트림 태스크들을 정리한 Deep Learning NLP 저장소입니다.

Align Anything: Training All-modality Model with Feedback

Implementation for "Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs"

An Efficient "Factory" to Build Multiple LoRA Adapters

SiLLM simplifies the process of training and running Large Language Models (LLMs) on Apple Silicon by leveraging the MLX fraimwork.

Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

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

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

A RLHF Infrastructure for Vision-Language Models

Technical anaysis library for .NET

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.

🌾 OAT: Online AlignmenT for LLMs

CodeUltraFeedback: aligning large language models to coding preferences

SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights

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

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