Use PEFT or Full-parameter to finetune 350+ LLMs or 90+ 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.

An Efficient "Factory" to Build Multiple LoRA Adapters

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

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

Technical anaysis library for .NET

Align Anything: Training All-modality Model with Feedback

A RLHF Infrastructure for Vision-Language Models

CodeUltraFeedback: aligning large language models to coding preferences

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.

Various training, inference and validation code and results related to Open LLM's that were pretrained (full or partially) on the Dutch language.

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

基于DPO算法微调语言大模型，简单好上手。

Data and models for the paper "Configurable Safety Tuning of Language Models with Synthetic Preference Data"

Examples for using the SiLLM framework for training and running Large Language Models (LLMs) on Apple Silicon