News666520/Qwen2.5-1.5B-Instruct

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 3, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Qwen2.5-1.5B-Instruct is a 1.54 billion parameter instruction-tuned causal language model developed by Qwen, part of the Qwen2.5 series. This model features a transformer architecture with RoPE, SwiGLU, and RMSNorm, supporting a context length of 32,768 tokens. It offers significant improvements in coding, mathematics, instruction following, long text generation, and structured data understanding, making it suitable for diverse chatbot and structured output tasks.

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Qwen2.5-1.5B-Instruct: An Enhanced Language Model

Qwen2.5-1.5B-Instruct is an instruction-tuned model from the Qwen2.5 series, developed by Qwen. This 1.54 billion parameter causal language model builds upon the Qwen2 architecture, incorporating transformers with RoPE, SwiGLU, and RMSNorm. It is designed for robust performance across various NLP tasks, featuring a substantial context window and improved generation capabilities.

Key Capabilities and Improvements

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates stronger adherence to instructions and is more resilient to diverse system prompts, benefiting role-play and chatbot implementations.
  • Long Text & Structured Data: Excels at generating long texts (up to 8K tokens) and understanding structured data like tables, with improved JSON output generation.
  • Extended Context: Supports a full context length of 32,768 tokens, allowing for processing and understanding of extensive inputs.
  • Multilingual Support: Provides robust support for over 29 languages, including major global languages like Chinese, English, French, Spanish, German, and Japanese.

Ideal Use Cases

This model is particularly well-suited for applications requiring:

  • Code Generation and Mathematical Problem Solving: Due to its specialized enhancements in these domains.
  • Advanced Chatbots and Assistants: Benefiting from improved instruction following and resilience to system prompts.
  • Structured Data Processing: Generating and understanding structured outputs like JSON and handling tabular data.
  • Long-form Content Generation: Capable of producing coherent and extended text outputs.
  • Multilingual Applications: Supporting a broad range of languages for global deployment.

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