Hugging Face Overview
Overview of OpenR1-Qwen-7B-Turkish
WiroAI/OpenR1-Qwen-7B-Turkish is a 7.6 billion parameter language model developed by WiroAI, built upon the Qwen2.5-Instruct architecture. It has been specifically fine-tuned using the WiroAI/dolphin-r1-turkish dataset over two epochs, with a maximum sequence length of 4096 tokens during training. The primary motivation behind this model is to enhance reasoning capabilities in Turkish, particularly for low-resource language contexts where other models may default to English or Chinese.
Key Capabilities
- Improved Turkish Reasoning: The model demonstrates a clearer and more effective reasoning process in Turkish compared to some other distilled models.
- Extensive Context Handling: While trained with a max sequence length of 4096, the model supports an impressive context length of 131072 tokens, allowing for processing of very long inputs.
- Open-Source Contribution: This model aims to contribute to the open-source community by providing a specialized Turkish language model.
Usage Considerations
- Experimental Nature: The model is developed with experimental motives, and further benchmark evaluations are encouraged.
- Token Generation: It is designed to produce more tokens than typical models, potentially consuming more VRAM during inference. For optimal results, ensure the model is allowed to generate a sufficient number of tokens (e.g., up to 4000 tokens for reasoning tasks).