LIF1014/ptdbench-Qwen2.5-0.5B-Instruct

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 16, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

LIF1014/ptdbench-Qwen2.5-0.5B-Instruct is a 0.49 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 improved capabilities in coding, mathematics, and instruction following. This model excels at generating long texts, understanding structured data like tables, and producing structured outputs such as JSON, while also offering robust multilingual support for over 29 languages.

Loading preview...

Qwen2.5-0.5B-Instruct Overview

This model is the instruction-tuned 0.5 billion parameter variant from the Qwen2.5 series, developed by Qwen. It builds upon previous Qwen models with significant enhancements across several key areas. The architecture is a transformer-based causal language model, featuring RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings, with a full context length of 32,768 tokens and generation capability up to 8,192 tokens.

Key Capabilities and Improvements

  • Enhanced Knowledge and Reasoning: Significantly improved performance in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates substantial improvements in adhering to instructions and generating long-form text (over 8K tokens).
  • Structured Data Handling: Better at understanding structured data, including tables, and generating structured outputs like JSON.
  • System Prompt Resilience: More robust to diverse system prompts, which enhances role-play and chatbot condition-setting.
  • Multilingual Support: Supports 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 efficient instruction following, structured output generation, and tasks involving coding or mathematical reasoning, especially where a smaller, faster model with a long context window is beneficial. Its multilingual capabilities also make it suitable for global applications.

Finetunes
1 models