pytransformer1/iol-smoke

TEXT GENERATIONConcurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 12, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The pytransformer1/iol-smoke model is an instruction-tuned 1.54 billion parameter causal language model from the Qwen2.5 series, developed by Qwen Team. It features a 32,768 token context length and is significantly improved in coding, mathematics, and instruction following compared to previous Qwen models. This model excels at generating long texts, understanding structured data like JSON, and offers robust multilingual support across 29 languages.

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Overview

pytransformer1/iol-smoke is an instruction-tuned model from the Qwen2.5 series, developed by Qwen Team. This 1.54 billion parameter causal language model builds upon the Qwen2 architecture, incorporating transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings. It supports a full 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, leveraging specialized expert models.
  • Instruction Following: Demonstrates substantial improvements in adhering to instructions and generating structured outputs, particularly JSON.
  • Long Text Generation: Excels at generating extended texts, handling outputs over 8,000 tokens effectively.
  • Structured Data Understanding: Better at processing and understanding structured data, such as tables.
  • Multilingual Support: Provides robust support for over 29 languages, including major global languages like Chinese, English, French, Spanish, and Japanese.
  • System Prompt Resilience: More resilient to diverse system prompts, enhancing its utility for role-play and chatbot condition-setting.

Good For

  • Applications requiring strong coding and mathematical reasoning at a smaller scale.
  • Tasks that demand precise instruction following and structured output generation (e.g., JSON).
  • Chatbot implementations needing resilient system prompt handling and role-play capabilities.
  • Generating long-form content and processing extensive contexts.
  • Multilingual applications across a broad range of languages.

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