aertsimon90/Qwen2.5-7B-Instruct

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 8, 2026License:apache-2.0Architecture:Transformer 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. This model significantly enhances capabilities in coding, mathematics, and instruction following, while also improving long text generation and structured output, including JSON. It supports a full context length of 131,072 tokens and generation up to 8,192 tokens, with multilingual support for over 29 languages.

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Overview

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

Key Capabilities

  • Enhanced Knowledge & Reasoning: Significantly improved performance in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates stronger instruction adherence and resilience to diverse system prompts, beneficial for role-play and chatbot conditioning.
  • Long Text Handling: Excels at generating long texts (over 8K tokens) and supports an impressive context length of up to 131,072 tokens, with generation up to 8,192 tokens. YaRN technique can be enabled for processing even longer texts.
  • Structured Data & Output: Improved understanding of structured data like tables and enhanced generation of structured outputs, particularly JSON.
  • Multilingual Support: Offers robust support for over 29 languages, including major global languages like Chinese, English, French, Spanish, German, and Japanese.

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.
  • Generating structured data formats like JSON.
  • Multilingual applications across a broad range of languages.

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