jerryyy45/Qwen2.5-3B-Instruct
TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 13, 2026License:qwen-researchArchitecture:Transformer Featherless Exclusive Cold
The jerryyy45/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 designed with transformers architecture including RoPE, SwiGLU, and RMSNorm. This model significantly improves capabilities in coding, mathematics, instruction following, and generating long, structured texts like JSON, while also offering 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.
Key Capabilities and Improvements
- Enhanced Knowledge & Specialized Skills: Significantly improved capabilities in coding and mathematics, benefiting from specialized expert models.
- Instruction Following: Demonstrates notable improvements in adhering to instructions and understanding diverse system prompts, which enhances role-play and chatbot condition-setting.
- Text Generation & Structure: Excels at generating long texts (up to 8K tokens) and understanding/generating structured data, particularly JSON outputs.
- Context Length: Supports a long context window of up to 32,768 tokens for input and can generate up to 8,192 tokens.
- Multilingual Support: Offers robust support for over 29 languages, including major global languages like Chinese, English, French, Spanish, German, and Japanese.
- Architecture: Built on a transformer architecture incorporating RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
Ideal Use Cases
- Applications requiring strong code generation or mathematical problem-solving.
- Chatbots or agents needing resilient instruction following and role-play implementation.
- Tasks involving long-form content generation or structured data output (e.g., JSON).
- Multilingual applications targeting a broad range of languages.