minsu0567/Uni-IAD-R2-Qwen3.5_2-sc-GRPO
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-GRPO is a 4.5 billion parameter Qwen3.5 model developed by minsu0567. This model was finetuned from minsu0567/Uni-IAD-R2-Qwen3.5_2 using Unsloth and Huggingface's TRL library, enabling 2x faster training. It features a 32768 token context length, making it suitable for applications requiring efficient processing of long sequences.
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Model Overview
The minsu0567/Uni-IAD-R2-Qwen3.5_2-sc-GRPO is a 4.5 billion parameter language model, developed by minsu0567. It is a finetuned variant of the Qwen3.5 architecture, specifically building upon the minsu0567/Uni-IAD-R2-Qwen3.5_2 model.
Key Characteristics
- Architecture: Based on the Qwen3.5 model family.
- Parameter Count: 4.5 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Efficiency: The model was trained with a focus on efficiency, utilizing Unsloth and Huggingface's TRL library, which reportedly enabled 2x faster finetuning.
Potential Use Cases
- Applications requiring efficient finetuning: Developers looking for a Qwen3.5-based model that benefits from optimized training techniques.
- Long-context tasks: Its 32768 token context length makes it suitable for processing and generating longer texts, code, or complex documents.
- Research and development: As a finetuned model, it can serve as a base for further experimentation and domain-specific adaptations.