aaasdsdfefsdfe/Qwen2.5-7B-Instruct

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Mar 22, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

Qwen2.5-7B-Instruct is a 7.61 billion parameter instruction-tuned causal language model developed by Qwen, featuring a transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It offers significant improvements in coding, mathematics, instruction following, and long text generation up to 8K tokens, with a context length of 131,072 tokens. This model excels at understanding structured data and generating structured outputs like JSON, and provides robust multilingual support for over 29 languages.

Loading preview...

Qwen2.5-7B-Instruct: Enhanced Multilingual LLM

Qwen2.5-7B-Instruct is an instruction-tuned causal language model from the Qwen2.5 series, developed by Qwen. This 7.61 billion parameter model builds upon its predecessor, Qwen2, with substantial improvements across several key areas. It leverages a transformer architecture incorporating RoPE, SwiGLU, RMSNorm, and Attention QKV bias.

Key Capabilities

  • Enhanced Reasoning: Significantly improved capabilities in coding and mathematics, benefiting from specialized expert models.
  • Instruction Following: Demonstrates better instruction adherence and resilience to diverse system prompts, aiding in role-play and chatbot condition-setting.
  • Long Context & Generation: Supports a full context length of 131,072 tokens and can generate up to 8,192 tokens. It utilizes YaRN for efficient long-text processing.
  • Structured Data & Output: Excels at understanding structured data (e.g., tables) and generating structured outputs, particularly JSON.
  • Multilingual Support: Offers robust support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic.

Good For

  • Applications requiring strong coding and mathematical reasoning.
  • Chatbots and agents needing precise instruction following and role-play capabilities.
  • Tasks involving long document summarization or generation.
  • Scenarios demanding structured data processing and JSON output generation.
  • Multilingual applications targeting a broad range of languages.

Finetunes
1 models