dennis19319/qwen2-5-14b-merged
The dennis19319/qwen2-5-14b-merged model is an instruction-tuned 14.7 billion parameter causal language model from the Qwen2.5 series, developed by Qwen. It features a transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias, supporting a context length of up to 131,072 tokens. This model significantly improves capabilities in coding, mathematics, instruction following, and generating long, structured texts like JSON, making it suitable for complex conversational AI and data processing tasks.
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Qwen2.5-14B-Instruct Overview
This model is an instruction-tuned variant of the Qwen2.5 series, developed by Qwen, featuring 14.7 billion parameters. It builds upon the Qwen2 architecture, incorporating improvements in several key areas. The model supports an extensive context length of up to 131,072 tokens, with generation capabilities up to 8,192 tokens, and includes multilingual support for over 29 languages.
Key Capabilities
- Enhanced Knowledge & Reasoning: Significantly improved performance in coding and mathematics, leveraging specialized expert models.
- Instruction Following: Demonstrates stronger adherence to instructions and is more resilient to diverse system prompts, aiding in role-play and chatbot implementations.
- Long Text & Structured Data Handling: Excels at generating long texts and understanding/generating structured outputs, particularly JSON.
- Multilingual Support: Covers a broad range of languages including Chinese, English, French, Spanish, German, and more.
- Architecture: Utilizes a transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
Good For
- Applications requiring advanced coding and mathematical reasoning.
- Chatbots and conversational agents needing robust instruction following and role-play capabilities.
- Tasks involving the generation or understanding of long, complex texts and structured data formats like JSON.
- Multilingual applications across its supported languages.