usernone1234/qwen2.5-1.5b-psychology-merged

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kPublished:May 16, 2026License:apache-2.0Architecture:Transformer Open Weights Warm

The usernone1234/qwen2.5-1.5b-psychology-merged model is an instruction-tuned 1.54 billion parameter 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, and producing structured outputs like JSON, while also offering robust multilingual support for over 29 languages.

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Overview

This repository hosts the usernone1234/qwen2.5-1.5b-psychology-merged model, an instruction-tuned variant of the Qwen2.5 series. Developed by Qwen, this model is a 1.54 billion parameter causal language model built on a transformer architecture with RoPE, SwiGLU, RMSNorm, and tied word embeddings. It supports a substantial context length of 32,768 tokens and can generate up to 8,192 tokens.

Key Capabilities

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics due to 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, handling outputs over 8,000 tokens.
  • Structured Data & Output: Improved understanding of structured data (e.g., tables) and generation of 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.

Good For

  • Applications requiring strong instruction following and structured output generation.
  • Tasks involving coding and mathematical reasoning.
  • Generating long-form content or processing extensive contexts.
  • Multilingual applications across a broad range of languages.

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