fahim8401/Qwen2.5-7B-Instruct

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Apr 14, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

Qwen2.5-7B-Instruct is a 7.61 billion parameter instruction-tuned causal language model from the Qwen2.5 series, developed by Qwen. This model significantly enhances capabilities in coding, mathematics, and instruction following, building upon the Qwen2 architecture. It features a 32K token context length (extensible to 128K with YaRN) and robust multilingual support for over 29 languages, making it suitable for diverse applications requiring advanced reasoning and structured output generation.

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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, developed by Qwen. This 7.61 billion parameter model (6.53B non-embedding) is built on a transformer architecture utilizing RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It represents a significant advancement over Qwen2, offering enhanced performance across several key areas.

Key Capabilities & Improvements

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates substantial improvements in adhering to instructions and generating structured outputs, particularly JSON.
  • Long Text Generation: Excels at generating long texts, supporting outputs over 8K tokens.
  • Structured Data Understanding: Better at interpreting structured data like tables.
  • System Prompt Resilience: More robust to diverse system prompts, improving role-play and chatbot condition-setting.
  • Context Length: Supports a full context length of 131,072 tokens (with YaRN scaling) and can generate up to 8,192 tokens.
  • Multilingual Support: Provides 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.
  • Precise instruction following and structured output generation (e.g., JSON).
  • Handling and generating long documents or conversations.
  • Multilingual interactions across a broad range of languages.
  • Robust chatbot implementations with complex role-play or condition settings.

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