minsu0567/Uni-IAD-R2-Qwen3.5_2-sc-GRPO3
The minsu0567/Uni-IAD-R2-Qwen3.5_2-sc-GRPO3 is a 4.5 billion parameter Qwen3.5 model developed by minsu0567, fine-tuned from minsu0567/Uni-IAD-R2-Qwen3.5_2. This model was trained using Unsloth and Huggingface's TRL library, achieving a 2x speed improvement during the fine-tuning process. It features a 32768 token context length and is licensed under Apache-2.0, making it suitable for applications requiring efficient fine-tuned Qwen3.5 capabilities.
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Model Overview
The minsu0567/Uni-IAD-R2-Qwen3.5_2-sc-GRPO3 is a 4.5 billion parameter language model developed by minsu0567. It is a fine-tuned variant of the Qwen3.5 architecture, specifically building upon the minsu0567/Uni-IAD-R2-Qwen3.5_2 base model. This model boasts a substantial context length of 32768 tokens, allowing it to process and generate longer sequences of text.
Key Characteristics
- Architecture: Qwen3.5, fine-tuned from
minsu0567/Uni-IAD-R2-Qwen3.5_2. - Parameter Count: 4.5 billion parameters.
- Context Length: Supports up to 32768 tokens, enabling handling of extensive inputs and outputs.
- Training Efficiency: Fine-tuned with Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to standard methods.
- License: Released under the Apache-2.0 license, providing broad usage permissions.
Potential Use Cases
This model is well-suited for applications that benefit from a Qwen3.5-based architecture with enhanced training efficiency. Its large context window makes it particularly useful for tasks requiring deep understanding or generation of long documents, conversations, or code. The use of Unsloth for fine-tuning suggests an emphasis on performance and resource optimization, making it a strong candidate for deployment in environments where training speed and efficiency are critical.