ArchiveStudio/Qwen2.5-1.5B-Instruct

TEXT GENERATIONConcurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 5, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

ArchiveStudio/Qwen2.5-1.5B-Instruct is a 1.54 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 built on a transformer architecture with RoPE, SwiGLU, and RMSNorm. This model significantly improves capabilities in coding, mathematics, instruction following, and generating structured outputs like JSON, while also supporting over 29 languages.

Loading preview...

Qwen2.5-1.5B-Instruct Overview

ArchiveStudio/Qwen2.5-1.5B-Instruct is an instruction-tuned variant of the Qwen2.5 series, developed by Qwen. This 1.54 billion parameter causal language model is designed with a transformer architecture incorporating RoPE, SwiGLU, RMSNorm, and attention QKV bias. It supports a full context length of 32,768 tokens and can generate up to 8,192 tokens.

Key Capabilities & Improvements

Qwen2.5 models, including this 1.5B instruction-tuned version, offer significant enhancements over previous Qwen2 iterations:

  • Enhanced Knowledge & Reasoning: Greatly improved performance in coding and mathematics due to specialized expert models.
  • Instruction Following: Substantial improvements in adhering to instructions and generating long texts (over 8K tokens).
  • Structured Data Handling: Better understanding of structured data (e.g., tables) and improved generation of structured outputs, particularly JSON.
  • Robustness: More resilient to diverse system prompts, enhancing role-play and chatbot condition-setting.
  • Multilingual Support: Provides support for over 29 languages, including major global languages like Chinese, English, French, Spanish, and Japanese.

Ideal Use Cases

This model is particularly well-suited for applications requiring:

  • Code Generation and Assistance: Leveraging its improved coding capabilities.
  • Mathematical Problem Solving: Benefiting from enhanced mathematical reasoning.
  • Structured Output Generation: Generating JSON or other structured data formats reliably.
  • Multilingual Chatbots and Assistants: Utilizing its broad language support and robust instruction following for diverse user interactions.
  • Long-form Content Generation: Creating extended text outputs while maintaining coherence.

Finetunes
1 models