Santhoshini/iol-solver-v0

TEXT GENERATIONConcurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 8, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Santhoshini/iol-solver-v0 is an instruction-tuned 1.54 billion parameter causal language model from the Qwen2.5 series, developed by Qwen. It features a transformer architecture with RoPE, SwiGLU, and RMSNorm, supporting a 32,768-token context length. This model demonstrates significantly improved capabilities in coding, mathematics, instruction following, and generating structured outputs like JSON, making it suitable for diverse chatbot and analytical applications.

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Overview

Santhoshini/iol-solver-v0 is an instruction-tuned variant of the Qwen2.5 model, specifically the 1.5 billion parameter version. Developed by Qwen, this model is part of the latest Qwen2.5 series, which builds upon Qwen2 with substantial enhancements across several key areas. It utilizes a transformer architecture incorporating RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.

Key Capabilities

  • Enhanced Knowledge & Reasoning: Significantly improved performance in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates better adherence to instructions and resilience to diverse system prompts, aiding in role-play and chatbot condition-setting.
  • Long Text Generation: Capable of generating long texts, exceeding 8,000 tokens.
  • Structured Data & Output: Improved understanding of structured data (e.g., tables) and generation of structured outputs, particularly JSON.
  • Multilingual Support: Supports over 29 languages, including major global languages like Chinese, English, French, Spanish, German, and Japanese.
  • Extended Context: Features a full context length of 32,768 tokens, with generation capabilities up to 8,192 tokens.

Good For

This model is well-suited for applications requiring strong performance in coding, mathematical problem-solving, and robust instruction following. Its ability to handle long contexts and generate structured outputs makes it valuable for chatbots, data processing, and tasks demanding precise, formatted responses.

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