ashphisher/Qwen2.5-3B-Instruct

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 28, 2026License:otherArchitecture:Transformer Featherless Exclusive Cold

The ashphisher/Qwen2.5-3B-Instruct is a 3.09 billion parameter instruction-tuned causal language model from the Qwen2.5 series, developed by Qwen. It features a 32,768 token context length and is significantly improved in coding, mathematics, and instruction following. This model excels at generating long texts, understanding structured data, and producing structured outputs like JSON, with robust multilingual support for over 29 languages.

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Qwen2.5-3B-Instruct Overview

This repository hosts the instruction-tuned 3.09 billion parameter Qwen2.5 model, part of the latest Qwen large language model series. Developed by Qwen, this model builds upon its predecessors with substantial enhancements across several key areas, making it a versatile choice for various applications.

Key Capabilities and Improvements

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics due to specialized expert models.
  • Superior Instruction Following: Demonstrates marked improvements in adhering to instructions, generating long texts (over 8K tokens), and understanding structured data such such as tables.
  • Robust Structured Output: Excels at generating structured outputs, particularly JSON, and is more resilient to diverse system prompts, aiding in role-play and chatbot condition-setting.
  • Extended Context & Multilingual Support: Features a full 32,768 token context length with the ability to generate up to 8,192 tokens. It also offers comprehensive multilingual support for over 29 languages, including major global languages like Chinese, English, French, Japanese, and Korean.
  • Architecture: Utilizes a transformer architecture with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.

When to Use This Model

This model is particularly well-suited for use cases requiring:

  • Code Generation and Mathematical Problem Solving: Leveraging its specialized training in these domains.
  • Complex Instruction Following: For applications needing precise adherence to user prompts and system instructions.
  • Long-form Content Generation: Capable of producing extended text outputs efficiently.
  • Structured Data Processing: Ideal for tasks involving the understanding and generation of structured formats like JSON or tables.
  • Multilingual Applications: Its broad language support makes it suitable for global deployments.

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