f0rc3ps/Qwen2.5-1.5B-Instruct

TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kPublished:Apr 6, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

f0rc3ps/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 enhanced with significantly more knowledge, improved coding and mathematics capabilities, and better instruction following. This model excels at generating long texts, understanding structured data like tables, and producing structured outputs such as JSON, with robust multilingual support for over 29 languages.

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Qwen2.5-1.5B-Instruct Overview

This model is the instruction-tuned 1.54 billion parameter variant from the Qwen2.5 series, developed by Qwen. It builds upon the Qwen2 architecture, incorporating transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings. The model supports a full context length of 32,768 tokens and can generate up to 8,192 tokens.

Key Capabilities & Improvements

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates substantial improvements in adhering to instructions and is more resilient to diverse system prompts, aiding in role-play and chatbot condition-setting.
  • Long Text Generation: Excels at generating extended texts, surpassing 8,000 tokens.
  • Structured Data & Output: Better at understanding structured data (e.g., tables) and generating structured outputs, particularly JSON.
  • Multilingual Support: Offers robust support for 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 suitable for applications requiring a compact yet powerful instruction-following LLM. Its strengths in coding, mathematics, long text generation, and structured output make it ideal for tasks such as:

  • Generating code snippets or mathematical solutions.
  • Creating detailed, lengthy responses or articles.
  • Processing and extracting information from structured data.
  • Producing JSON or other structured data formats.
  • Developing multilingual chatbots or assistants that require strong instruction adherence.