llama_hausa_LAFT

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Paper and Citation

Paper: Few-Shot Cross-Lingual Transfer for Prompting Large Language Models in Low-Resource Languages

@misc{toukmaji2024fewshot,
      title={Few-Shot Cross-Lingual Transfer for Prompting Large Language Models in Low-Resource Languages}, 
      author={Christopher Toukmaji},
      year={2024},
      eprint={2403.06018},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

hausa_finetuned_model

This model is a fine-tuned version of HF_llama on the mc4 ha dataset. It achieves the following results on the evaluation set:

• Loss: 1.4357
• Accuracy: 0.6728

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 4
• total_eval_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.29.2
• Pytorch 2.0.1+cu117
• Datasets 2.12.0
• Tokenizers 0.13.3