minsu0567/Uni-IAD-R2-Qwen3.5_2-sc-GRPO2

VISIONConcurrency Cost:1Model Size:4.5BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 4, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

The minsu0567/Uni-IAD-R2-Qwen3.5_2-sc-GRPO2 model is a 4.5 billion parameter Qwen3.5-based language model developed by minsu0567, fine-tuned from minsu0567/Uni-IAD-R2-Qwen3.5_2. It features a 32768 token context length and was trained using Unsloth, enabling a 2x faster training process. This model is optimized for efficient performance, leveraging Unsloth's speed enhancements.

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Model Overview

The minsu0567/Uni-IAD-R2-Qwen3.5_2-sc-GRPO2 is a 4.5 billion parameter language model based on the Qwen3.5 architecture, developed by minsu0567. It is a fine-tuned version of the minsu0567/Uni-IAD-R2-Qwen3.5_2 model, featuring a substantial context length of 32768 tokens.

Key Characteristics

  • Efficient Training: This model was trained with Unsloth, which significantly accelerated the training process by making it 2x faster.
  • Base Model: Built upon the Qwen3.5 architecture, suggesting strong general language understanding and generation capabilities.
  • Developer: Developed and maintained by minsu0567.
  • License: Distributed under the Apache-2.0 license, allowing for broad usage and modification.

Potential Use Cases

Given its efficient training and Qwen3.5 base, this model is suitable for applications requiring:

  • Fast Prototyping: The Unsloth-enabled training suggests it could be part of a workflow where rapid iteration and fine-tuning are beneficial.
  • General Language Tasks: Its foundation implies applicability to a wide range of NLP tasks, including text generation, summarization, and question answering.
  • Long Context Applications: The 32768 token context window makes it suitable for processing and understanding lengthy documents or conversations.

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