DownloadQwen2.5-14B-Instruct is a powerful instruction-tuned language model developed by the Qwen team, based on the Qwen2.5 architecture. It features 14.7 billion parameters and is optimized for tasks like dialogue, long-form generation, and structured output. The model supports context lengths up to 128K tokens and can generate up to 8K tokens, making it suitable for long-context applications. It demonstrates improved performance in coding, mathematics, and multilingual understanding across over 29 languages. Qwen2.5-14B-Instruct is built on a transformer backbone with RoPE, SwiGLU, RMSNorm, and attention QKV bias. It’s resilient to varied prompt styles and is especially effective for JSON and tabular data generation. The model is instruction-tuned and supports chat templating, making it ideal for chatbot and assistant use cases.
Features
- 14.7B parameter instruction-tuned model based on Qwen2.5
- Supports context lengths up to 128K tokens and outputs up to 8K
- Enhanced performance in coding, math, and structured data handling
- Resilient to varied system prompts for flexible assistant behavior
- Multilingual support for 29+ languages including Chinese, Spanish, and Arabic
- Advanced architecture with RoPE, SwiGLU, RMSNorm, and QKV bias
- Ideal for long-form generation, chatbots, and structured output tasks
- Compatible with Hugging Face Transformers and vLLM deployment
Project Samples
