Use PEFT or Full-parameter to finetune 400+ LLMs (Qwen2.5, Llama3.2, GLM4, Internlm2.5, Yi1.5, Mistral, Baichuan2, DeepSeek, ...) or 100+ 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, ...).

Paddle Multimodal Integration and eXploration, supporting mainstream multi-modal tasks, including end-to-end large-scale multi-modal pretrain models and diffusion model toolbox. Equipped with high performance and flexibility.

An open-source implementaion for fine-tuning Qwen2-VL series by Alibaba Cloud.

【grps接入trtllm】通过GPRS+TensorRT-LLM+Tokenizers.cpp实现纯C++版高性能OpenAI LLM服务，支持chat和function call模式，支持ai agent，支持分布式多卡推理，支持多模态，支持gradio聊天界面。

✨🦋 illufly 是自我进化的 Agent 框架: 基于自我进化，快速创造价值

视频理解：千问视频多模态模型 & Dify

A Python base cli tool for caption images with WD series, Joy-caption-pre-alpha,meta Llama 3.2 Vision Instruct and Qwen2 VL Instruct models.

Colaboratory上でQwenLM/Qwen2-VLをお試しするサンプル

Running Large Language Model easily.

This project demonstrates how to use the Qwen2-VL model from Hugging Face for Optical Character Recognition (OCR) and Visual Question Answering (VQA). The model combines vision and language capabilities, enabling users to analyze images and generate context-based responses.

Qwen2-VL在文旅领域的LLaMA-Factory微调案例 The case for fine-tuning Qwen2-VL in the field of historical literature and museums

A workshop for collections of multi-modal LLM examples, samples, reference architecture and demos on Amazon SageMaker.

An open-source server implementation for inference Qwen2-VL series model using fastapi.

基于多模态大模型的智能搜索助手，通过AI技术实现小红书平台的智能化信息检索和知识整合

"Smart Vision Technology for Quality Control" uses computer vision to automate product inspections, extracting details like product name, quantity, expiry date, and freshness from images. Built for Flipkart Grid 6.0, it enhances accuracy and efficiency in quality control, minimizing manual checks.

This repo contains the winning code for Amazon ML Challenge 2024. The challenge was to develop a Machine Learning model to extract product entity details directly from the product images.

practical projects using LLM, VLM and Diffusion models

OCR and Document Search Web Application

Qwen2-VLのデモNotebook