Qwen2.5 - A versatile collection of large language models developed by Alibaba Cloud's Qwen team.

## Overview of Qwen2.5 Qwen2.5 is a collection of large language models developed by Alibaba Cloud's Qwen team. It includes general language models in sizes ranging from 0.5B to 72B parameters, as well as specialized models like Qwen2.5-Coder for programming and Qwen2.5-Math for mathematical tasks. The models are designed for a wide range of applications, from edge devices to enterprise-level systems. ## Key Features of Qwen2.5 General Models The Qwen2.5 general models offer flexibility in sizes, ranging from 0.5B to 72B parameters. They provide advanced capabilities such as instruction following, long text generation, and structured data handling. Additionally, they support over 29 languages and are compatible with various frameworks like llama.cpp and vLLM for local running and deployment. ## Applications of Qwen2.5 Models Qwen2.5 models are primarily used for natural language understanding, generation, and instruction-based tasks. They are suitable for content creation, data processing, and handling complex tasks that require long text generation and structured data understanding. Their multilingual capabilities make them versatile tools for global applications. ## Accessing Qwen2.5 Models Users can access and download Qwen2.5 models from their individual pages linked in the collection on Hugging Face. For local inference, libraries such as llama.cpp, Ollama, and MLX-LM are supported. Deployment can be facilitated through serving frameworks like vLLM and TGI. Tool use is enabled via Qwen-Agent, and finetuning can be performed using frameworks like Axolotl for specific use cases. ## Technical Specifications of Qwen2.5 Models Qwen2.5 models come in sizes ranging from 0.5B to 72B parameters. They are pretrained on up to 18T tokens and support context lengths up to 128K tokens, with generation capabilities up to 8K tokens. The models are enhanced for instruction following, long text generation, and structured data understanding. They support over 29 languages and are compatible with various local running and deployment frameworks. ### Citation sources: - [Qwen2.5](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e) - Official URL Updated: 2025-03-26