EmmaScharfmann/sustainability-robotics-classifier-qwen2.5-7b
The EmmaScharfmann/sustainability-robotics-classifier-qwen2.5-7b is a 7.6 billion parameter Qwen2.5 model developed by EmmaScharfmann. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for classification tasks related to sustainability and robotics, leveraging its Qwen2.5 architecture for specialized performance.
Loading preview...
Model Overview
This model, EmmaScharfmann/sustainability-robotics-classifier-qwen2.5-7b, is a 7.6 billion parameter Qwen2.5-based classifier developed by EmmaScharfmann. It was fine-tuned from the unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit base model.
Key Characteristics
- Architecture: Based on the Qwen2.5 model family.
- Parameter Count: Features 7.6 billion parameters, providing a balance of capability and efficiency.
- Training Efficiency: The model was trained significantly faster using Unsloth and Huggingface's TRL library, indicating an optimized fine-tuning process.
Primary Use Case
This model is specifically designed for classification tasks within the domains of sustainability and robotics. Its fine-tuning process suggests an optimization for understanding and categorizing information relevant to these technical fields.