Tu522004/RD-9B-Distill
VISIONConcurrency Cost:1Model Size:9BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:May 13, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Tu522004/RD-9B-Distill is a 9 billion parameter language model developed by Tu522004, finetuned from Qwen/Qwen3.5-9B. This model was trained significantly faster using Unsloth and Huggingface's TRL library, offering efficient performance for various language generation tasks. With a 32768 token context length, it is suitable for applications requiring processing of longer inputs.
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Tu522004/RD-9B-Distill: A Faster Finetuned Qwen3.5 Model
Tu522004/RD-9B-Distill is a 9 billion parameter language model, finetuned by Tu522004 from the robust Qwen/Qwen3.5-9B base model. This iteration stands out due to its optimized training process, which was completed 2x faster by leveraging the Unsloth library in conjunction with Huggingface's TRL library.
Key Characteristics
- Base Model: Finetuned from Qwen/Qwen3.5-9B, inheriting its strong foundational capabilities.
- Efficient Training: Utilizes Unsloth for accelerated finetuning, demonstrating efficiency in model development.
- Parameter Count: Features 9 billion parameters, balancing performance with computational requirements.
- Context Length: Supports a substantial context window of 32768 tokens, enabling the processing of extensive inputs.
Potential Use Cases
- General Language Generation: Suitable for a wide array of text generation tasks, from creative writing to summarization.
- Applications Requiring Long Context: Its large context window makes it ideal for tasks like document analysis, extended dialogue, or code comprehension.
- Efficient Deployment: The optimized training suggests potential for more resource-efficient deployment compared to models with longer training cycles.