Orange/Wolof-Qwen2.5-7B-it-v2-fc-v2-conv-v1_2epochs
Orange/Wolof-Qwen2.5-7B-it-v2-fc-v2-conv-v1_2epochs is a 7.6 billion parameter instruction-tuned language model developed by Orange, based on the Qwen2.5-7B-Instruct architecture. This model is specifically fine-tuned for the Wolof language, leveraging a diverse set of Wolof language datasets for instruction tuning. It excels in conversational and instruction-following tasks within the Wolof linguistic context, making it suitable for applications requiring Wolof language understanding and generation.
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Model Overview
Orange/Wolof-Qwen2.5-7B-it-v2-fc-v2-conv-v1_2epochs is a 7.6 billion parameter instruction-tuned model developed by Orange. It is built upon the Qwen2.5-7B-Instruct base model and has been extensively fine-tuned using a variety of Wolof language datasets. The model's training focused on instruction-following and conversational capabilities, specifically within the Wolof linguistic domain.
Key Capabilities
- Wolof Language Proficiency: Specialized instruction tuning on multiple Wolof datasets, including translated Alpaca, Dolly-15k, SAMSum dialogues, and various Wolof-specific conversational and instruction datasets.
- Instruction Following: Designed to understand and respond to instructions in Wolof.
- Conversational AI: Optimized for generating coherent and contextually relevant responses in Wolof conversations.
- Function Calling: Includes training data for function calling instructions in English, suggesting potential for integration with tools.
Good For
- Applications requiring natural language understanding and generation in the Wolof language.
- Developing chatbots or virtual assistants that interact with Wolof-speaking users.
- Research and development in low-resource language NLP, specifically for Wolof.
- Tasks involving instruction-following and conversational AI in a Wolof context.