Puttimet/Qwen2.5-7B-Admin-NongKhanom-Full
Puttimet/Qwen2.5-7B-Admin-NongKhanom-Full is a 7.6 billion parameter Qwen2.5-based causal language model developed by Puttimet. This model was fine-tuned from unsloth/Qwen2.5-7B-Instruct-bnb-4bit using Unsloth and Huggingface's TRL library, enabling faster training. It features a 32768 token context length and is optimized for specific administrative tasks, leveraging its efficient fine-tuning process.
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Model Overview
Puttimet/Qwen2.5-7B-Admin-NongKhanom-Full is a 7.6 billion parameter language model based on the Qwen2.5 architecture. It was developed by Puttimet and fine-tuned from the unsloth/Qwen2.5-7B-Instruct-bnb-4bit model. A key differentiator for this model is its training methodology, which utilized Unsloth and Huggingface's TRL library, resulting in a 2x faster fine-tuning process.
Key Capabilities
- Efficient Fine-tuning: Leverages Unsloth for accelerated training, making it resource-efficient for further adaptations.
- Qwen2.5 Architecture: Benefits from the robust base capabilities of the Qwen2.5 model family.
- Extended Context: Supports a context length of 32768 tokens, suitable for processing longer inputs.
Good For
- Administrative Tasks: Designed for specific administrative applications, though the exact nature is not detailed in the README.
- Developers seeking efficient fine-tuning: Ideal for those who prioritize faster training times for custom applications.
- Applications requiring a Qwen2.5 base: Suitable for use cases that align with the strengths of the Qwen2.5 model family.