ArchiveStudio/Qwen2.5-7B-Instruct

TEXT GENERATIONConcurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 5, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

ArchiveStudio/Qwen2.5-7B-Instruct is a 7.61 billion parameter instruction-tuned causal language model developed by Qwen. This model significantly improves capabilities in coding, mathematics, instruction following, and generating long texts up to 8K tokens. It also offers enhanced understanding of structured data like tables and robust multilingual support for over 29 languages. The model is designed for diverse applications requiring strong reasoning, structured output, and long-context processing.

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Qwen2.5-7B-Instruct Overview

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

Key Capabilities & Improvements

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates marked improvements in adhering to instructions and generating structured outputs, including JSON.
  • Long-Context Processing: Supports a full context length of 131,072 tokens and can generate up to 8,192 tokens. It utilizes YaRN for efficient handling of extensive inputs.
  • Multilingual Support: Offers robust support for over 29 languages, including Chinese, English, French, Spanish, and more.
  • Structured Data Understanding: Better at understanding structured data formats like tables and is more resilient to diverse system prompts for role-play and chatbot conditions.

When to Use This Model

This model is particularly well-suited for applications requiring:

  • Advanced coding and mathematical problem-solving.
  • Precise instruction following and structured output generation.
  • Processing and generating long texts.
  • Multilingual interactions and content creation.
  • Chatbot implementations demanding resilient role-play and condition setting.

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