israel/AfriGuard-AfriqueQwen3.5-4B
AfriGuard-AfriqueQwen3.5-4B is a 4.5 billion parameter language model developed by israel, fine-tuned from McGill-NLP's AfriqueQwen3.5-4B. This model is specifically adapted using the afriguard dataset, suggesting an optimization for tasks related to that dataset's domain. Its primary differentiator lies in its specialized fine-tuning for a particular dataset, making it suitable for applications requiring domain-specific knowledge derived from the afriguard data.
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AfriGuard-AfriqueQwen3.5-4B Overview
AfriGuard-AfriqueQwen3.5-4B is a 4.5 billion parameter language model developed by israel. It is a fine-tuned iteration of the McGill-NLP/AfriqueQwen3.5-4B base model, specifically adapted using the afriguard dataset. This specialization indicates its potential for tasks and applications within the domain covered by the afriguard data.
Key Capabilities
- Domain-Specific Adaptation: Fine-tuned on the afriguard dataset, suggesting enhanced performance for tasks relevant to that data's characteristics.
- Base Model Foundation: Built upon the robust AfriqueQwen3.5-4B architecture, providing a strong linguistic and generative foundation.
Good For
- Specialized Applications: Ideal for use cases that benefit from a model trained on the afriguard dataset.
- Research and Development: Suitable for researchers exploring the impact of domain-specific fine-tuning on pre-trained language models, particularly with the afriguard dataset.
Training Details
The model was trained with a learning rate of 1e-05, a batch size of 1, and a cosine learning rate scheduler over 0.5 epochs. The training utilized multi-GPU distribution and an AdamW optimizer.