sanketking02232/Qwen2.5-7B-Instruct

TEXT GENERATIONConcurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 6, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

Qwen2.5-7B-Instruct is a 7.61 billion parameter instruction-tuned causal language model developed by Qwen, part of the Qwen2.5 series. It features a transformer architecture with RoPE, SwiGLU, and RMSNorm, supporting a 32K context length (extensible to 128K with YaRN). This model demonstrates significantly improved capabilities in coding, mathematics, instruction following, long text generation, and structured data understanding, making it suitable for diverse multilingual applications across 29 languages.

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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 the Qwen2 architecture, incorporating improvements in knowledge, coding, and mathematical reasoning. The model supports a substantial context length of 32,768 tokens, which can be extended up to 128,000 tokens using the YaRN technique for processing longer texts.

Key Capabilities

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates strong instruction following, generating long texts (over 8K tokens), and understanding structured data like tables.
  • Structured Output: Excels at generating structured outputs, particularly JSON, and is more resilient to diverse system prompts for role-play and chatbot conditions.
  • Multilingual Support: Offers robust 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 well-suited for applications requiring:

  • Advanced Coding and Math: Its specialized training makes it effective for tasks in these domains.
  • Long-form Content Generation: Capable of generating extensive texts and handling long input contexts.
  • Structured Data Processing: Ideal for scenarios involving the understanding of structured data and generating structured outputs.
  • Multilingual Applications: Its broad language support makes it versatile for global use cases.

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