TreezzZ/opd-teacher-webshop-7b
Qwen2.5-7B-Instruct is a 7.6 billion parameter instruction-tuned causal language model developed by Qwen. This model significantly improves upon Qwen2 in knowledge, coding, and mathematics, and excels at instruction following, long text generation (up to 8K tokens), and understanding structured data like JSON. It supports a full context length of 131,072 tokens and is multilingual, supporting over 29 languages.
Loading preview...
Overview
Qwen2.5-7B-Instruct is a 7.6 billion parameter instruction-tuned causal language model from the Qwen2.5 series, developed by Qwen. It builds upon Qwen2 with substantial enhancements across several key areas. The model features a transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias, and is designed for both pretraining and post-training stages.
Key Capabilities
- Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics, leveraging specialized expert models.
- Instruction Following: Stronger instruction adherence and resilience to diverse system prompts, beneficial for role-play and chatbot implementations.
- Long Text Handling: Improved generation of long texts (up to 8K tokens) and understanding of structured data, including JSON outputs. It supports a full context length of 131,072 tokens, with a generation length of 8,192 tokens, utilizing YaRN for long text extrapolation.
- Multilingual Support: Comprehensive support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic.
When to Use This Model
This model is particularly well-suited for applications requiring:
- Advanced coding and mathematical problem-solving.
- Robust instruction following and structured output generation (e.g., JSON).
- Processing and generating extensive textual content.
- Multilingual interactions across a broad range of languages.