minsu0567/Uni-IAD-R2-Qwen3.5-GRPO-answer_last2
The minsu0567/Uni-IAD-R2-Qwen3.5-GRPO-answer_last2 is a 4.5 billion parameter Qwen3.5-based causal 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 specifically optimized for tasks related to answering based on the last part of a sequence, building upon the Uni-IAD-R2-Qwen3.5-answer-last2 model.
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Model Overview
The minsu0567/Uni-IAD-R2-Qwen3.5-GRPO-answer_last2 is a 4.5 billion parameter language model built upon the Qwen3.5 architecture, developed by minsu0567. It features a substantial context length of 32768 tokens, making it suitable for processing longer inputs.
Key Characteristics
- Base Model: Qwen3.5
- Parameter Count: 4.5 billion
- Context Length: 32768 tokens
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process compared to standard methods.
- Origin: This model is a further fine-tuned version of the
minsu0567/Uni-IAD-R2-Qwen3.5-answer-last2model.
Intended Use
This model is designed for tasks that require generating answers or responses based on the latter part of an input sequence, leveraging its specialized fine-tuning. Its efficient training process and substantial context window make it a robust choice for applications requiring focused response generation from extensive textual data.