Use PEFT or Full-parameter to finetune 450+ LLMs (Qwen2.5, InternLM3, GLM4, Llama3.3, Mistral, Yi1.5, Baichuan2, DeepSeek-R1, ...) and 150+ MLLMs (Qwen2.5-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, ...).

streamline the fine-tuning process for multimodal models: PaliGemma 2, Florence-2, and Qwen2.5-VL

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 and Qwen2.5-VL series by Alibaba Cloud.

Higher performance OpenAI LLM service than vLLM serve: A pure C++ high-performance OpenAI LLM service implemented with GPRS+TensorRT-LLM+Tokenizers.cpp, supporting chat and function call, AI agents, distributed multi-GPU inference, multimodal capabilities, and a Gradio chat interface.

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

Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

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

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

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

Running Large Language Model easily.

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

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

we finetune unsloth llama model to extract mathematical fomulas in the images with optical character recognition(OCR)

"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.