Qwen is a comprehensive framework for large language model development, serving, and deployment. It provides a complete ecosystem for transformer-based sequence modeling, offering base models alongside specialized tools for instruction-tuned alignment, fine-tuning, and long-context inference. The project is designed to support both research and production environments, enabling users to train, optimize, and host generative models locally or across distributed hardware.
The framework distinguishes itself through its focus on high-performance serving and extensibility. It features a high-performance inference engine that exposes OpenAI-compatible HTTP endpoints, allowing for integration into existing application architectures. To support complex workflows, it includes native capabilities for agentic tool use and function calling, which can be further refined through dedicated fine-tuning processes.
The platform covers a broad range of operational requirements, including model quantization, multi-device tensor parallelism, and memory-efficient key-value caching to optimize throughput and resource usage. It also provides robust utilities for benchmarking performance, managing system-level behaviors, and securing model endpoints through authentication and safety-aligned configurations.
The repository includes extensive documentation and scripts for model weight conversion, vocabulary expansion, and deployment across both CPU and GPU hardware.
