Use PEFT or Full-parameter to finetune 400+ LLMs or 100+ MLLMs. (LLM: Qwen2.5, Llama3.2, GLM4, Internlm2.5, Yi1.5, Mistral, Baichuan2, DeepSeek, Gemma2, ...; MLLM: Qwen2-VL, Qwen2-Audio, Llama3.2-Vision, Llava, InternVL2, MiniCPM-V-2.6, GLM4v, Xcomposer2.5, Yi-VL, DeepSeek-VL, Phi3.5-Vision, ...)

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.

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

An Efficient "Factory" to Build Multiple LoRA Adapters

Align Anything: Training All-modality Model with Feedback

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

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

Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step

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

Technical anaysis library for .NET

A RLHF Infrastructure for Vision-Language Models

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

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

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

🌾 OAT: Online AlignmenT for LLMs

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