DownloadQwen2-7B-Instruct is a 7.62-billion-parameter instruction-tuned language model from the Qwen2 series developed by Alibaba's Qwen team. Built on a transformer architecture with SwiGLU activation and group query attention, it is optimized for chat, reasoning, coding, multilingual tasks, and extended context understanding up to 131,072 tokens. The model was pretrained on a large-scale dataset and aligned via supervised fine-tuning and direct preference optimization. It shows strong performance across benchmarks such as MMLU, MT-Bench, GSM8K, and Humaneval, often surpassing similarly sized open-source models. Designed for conversational use, it integrates with Hugging Face Transformers and supports long-context applications via YARN and vLLM for efficient deployment.
Features
- 7.62B parameters with instruction-tuning for chat tasks
- Supports ultra-long context windows (up to 131K tokens)
- Built with SwiGLU activation and QKV bias architecture
- Strong multilingual, coding, math, and reasoning capabilities
- Outperforms Qwen1.5 and many 7B–9B open-source models
- Easily deployable with Hugging Face Transformers or vLLM
- Apache 2.0 licensed and openly available
- Trained using both supervised fine-tuning and preference optimization
Project Samples
