Neura-Tech-AI/Qwen2.5-7B-Instruct

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Mar 29, 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. It significantly improves upon Qwen2 with enhanced knowledge, coding, and mathematical capabilities, alongside better instruction following and long text generation. The model supports a full context length of 131,072 tokens and generation up to 8,192 tokens, with multilingual support for over 29 languages, making it suitable for diverse conversational AI applications requiring robust performance across various tasks.

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Qwen2.5-7B-Instruct Overview

Qwen2.5-7B-Instruct is an instruction-tuned causal language model from the Qwen2.5 series, featuring 7.61 billion parameters. This model builds upon its predecessor, Qwen2, with substantial enhancements across several key areas.

Key Capabilities & Improvements

  • Expanded Knowledge & Specialized Skills: Significantly improved in general knowledge, coding, and mathematics, leveraging specialized expert models.
  • Enhanced Instruction Following: Demonstrates better adherence to instructions and is more resilient to diverse system prompts, improving role-play and condition-setting for chatbots.
  • Long Text Handling: Excels at generating long texts (up to 8,192 tokens) and understanding structured data like tables, with a full context length of 131,072 tokens.
  • Structured Output Generation: Improved ability to generate structured outputs, particularly JSON.
  • Multilingual Support: Supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic.
  • Architecture: Based on transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.

Long Context Processing

The model's config.json is set for a context length of 32,768 tokens. For inputs exceeding this, it utilizes YaRN for length extrapolation, ensuring optimal performance on lengthy texts. Users can enable YaRN by adding specific rope_scaling configurations to the config.json.