Tu522004/RD-9B-Distill-v2
VISIONConcurrency Cost:1Model Size:9BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Jun 19, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Tu522004/RD-9B-Distill-v2 is a 9 billion parameter Qwen3.5-based causal language model developed by Tu522004. This model was fine-tuned using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general language generation tasks, leveraging its efficient training methodology.
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Overview
Tu522004/RD-9B-Distill-v2 is a 9 billion parameter language model, fine-tuned by Tu522004. It is based on the Qwen3.5 architecture and was developed with a focus on training efficiency.
Key Characteristics
- Base Model: Qwen3.5-9B, providing a robust foundation for language understanding and generation.
- Efficient Training: Fine-tuned using Unsloth and Huggingface's TRL library, resulting in a 2x speedup during the training process.
- Context Length: Supports a context window of 32,768 tokens, enabling the model to process and generate longer sequences of text.
Good For
- Applications requiring a Qwen3.5-based model with optimized training.
- General text generation and understanding tasks where a 9B parameter model is suitable.
- Developers interested in models fine-tuned with efficient methods like Unsloth.