minsu0567/Uni-IAD-R2-Qwen3.5_2-mo-GRPO3

VISIONConcurrent Unit Cost:1Model Size:4.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 5, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The minsu0567/Uni-IAD-R2-Qwen3.5_2-mo-GRPO3 is a 4.5 billion parameter Qwen3.5-based causal language model developed by minsu0567, offering 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 understanding and generation tasks, leveraging its efficient training methodology.

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Model Overview

The minsu0567/Uni-IAD-R2-Qwen3.5_2-mo-GRPO3 is a 4.5 billion parameter language model based on the Qwen3.5 architecture, developed by minsu0567. It supports a substantial context length of 32768 tokens, making it suitable for processing longer sequences of text.

Key Characteristics

  • Base Model: Fine-tuned from minsu0567/Uni-IAD-R2-Qwen3.5_2.
  • Efficient Training: This model was fine-tuned with a focus on efficiency, utilizing Unsloth and Huggingface's TRL library. This approach enabled a 2x faster training speed compared to standard methods.
  • Parameter Count: Features 4.5 billion parameters, balancing performance with computational requirements.
  • Context Window: Offers a large 32768-token context window, beneficial for tasks requiring extensive contextual understanding.

Potential Use Cases

This model is well-suited for applications where efficient training and a large context window are advantageous, including:

  • General text generation and completion.
  • Summarization of long documents.
  • Question answering over extensive texts.
  • Tasks benefiting from the Qwen3.5 architecture's capabilities.

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