minsu0567/Uni-IAD-R2-Qwen3.5_2-mo-GRPO4

VISIONConcurrent Unit Cost:1Model Size:4.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 13, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The minsu0567/Uni-IAD-R2-Qwen3.5_2-mo-GRPO4 is a 4.5 billion parameter Qwen3.5-based language model developed by minsu0567, featuring a 32768 token context length. This model was fine-tuned using Unsloth and Huggingface's TRL library, resulting in a 2x faster training process. It is designed for general language tasks, leveraging its efficient training methodology.

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Overview

The minsu0567/Uni-IAD-R2-Qwen3.5_2-mo-GRPO4 is a 4.5 billion parameter language model, fine-tuned from the base minsu0567/Uni-IAD-R2-Qwen3.5_2 model. It features a substantial context length of 32768 tokens, making it suitable for processing longer inputs and generating more coherent, extended outputs.

Key Characteristics

  • Efficient Training: This model was fine-tuned with significant speed improvements, utilizing Unsloth and Huggingface's TRL library, achieving a 2x faster training time compared to conventional methods.
  • Qwen3.5 Architecture: Built upon the Qwen3.5 family, it inherits robust language understanding and generation capabilities.
  • Developer: Developed by minsu0567, indicating a specialized focus on its fine-tuning process.

Potential Use Cases

  • General Text Generation: Capable of various text generation tasks due to its base architecture and fine-tuning.
  • Applications Requiring Long Context: Its 32768 token context window makes it well-suited for tasks like summarization of lengthy documents, detailed question answering, or maintaining extended conversational threads.
  • Research and Development: Offers a foundation for further experimentation and fine-tuning, particularly for those interested in efficient training methodologies.

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2 models