nttruong1007/qb-qwen25-1p5b

TEXT GENERATIONConcurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 16, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The nttruong1007/qb-qwen25-1p5b 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 built on a transformer architecture with RoPE, SwiGLU, and RMSNorm. This model significantly improves upon previous Qwen versions in knowledge, coding, mathematics, instruction following, and generating structured outputs like JSON, making it suitable for diverse conversational AI and data processing tasks.

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Overview

The nttruong1007/qb-qwen25-1p5b is an instruction-tuned variant of the Qwen2.5 1.5 billion parameter model, part of the latest Qwen large language model series. Developed by Qwen, this model is built on a transformer architecture incorporating RoPE, SwiGLU, and RMSNorm, and supports a substantial context length of 32,768 tokens with an 8,192 token generation capacity. It represents a significant advancement over Qwen2 models, particularly in its enhanced knowledge base and specialized capabilities.

Key Capabilities

  • Enhanced Knowledge & Reasoning: Features greatly improved capabilities in coding and mathematics, benefiting from specialized expert models.
  • Instruction Following: Demonstrates significant improvements in adhering to instructions and generating long texts (over 8K tokens).
  • Structured Data & Output: Excels at understanding structured data, such as tables, and generating structured outputs, especially JSON.
  • Robustness: More resilient to diverse system prompts, which enhances role-play and condition-setting for chatbots.
  • Multilingual Support: Provides support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic.

Good For

This model is well-suited for applications requiring robust instruction following, accurate code and mathematical problem-solving, and the generation of structured data. Its multilingual support also makes it versatile for global applications, while its resilience to system prompts is beneficial for complex chatbot and role-playing scenarios.

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