israel/AfriGuard-AfriqueQwen3.5-4B-50Langs
AfriGuard-AfriqueQwen3.5-4B-50Langs is a 4.5 billion parameter language model developed by israel, fine-tuned from McGill-NLP/AfriqueQwen3.5-4B-50Langs. This model is specifically adapted using the afriguard dataset, making it suitable for applications requiring specialized knowledge from that domain. With a 32768 token context length, it is designed for tasks benefiting from extensive contextual understanding.
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Model Overview
AfriGuard-AfriqueQwen3.5-4B-50Langs is a 4.5 billion parameter language model, fine-tuned by israel from the base model McGill-NLP/AfriqueQwen3.5-4B-50Langs. This adaptation specifically leverages the afriguard dataset, indicating a specialization for tasks and applications within that domain. The model was trained with a learning rate of 1e-05 over 1 epoch, utilizing a multi-GPU setup and AdamW optimizer.
Key Training Details
- Base Model: McGill-NLP/AfriqueQwen3.5-4B-50Langs
- Fine-tuning Dataset:
afriguarddataset - Learning Rate: 1e-05
- Epochs: 1.0
- Optimizer: AdamW_TORCH_FUSED
- Framework Versions: Transformers 5.8.0, Pytorch 2.14.0+cu130, Datasets 4.0.0, Tokenizers 0.22.2
Intended Use Cases
Given its fine-tuning on the afriguard dataset, this model is likely best suited for:
- Applications requiring understanding or generation of content related to the
afriguarddomain. - Tasks where specialized knowledge from the fine-tuning data is beneficial.
Further details on specific intended uses and limitations would require more information about the afriguard dataset and the fine-tuning objectives.