ChrisToukmaji/laft_hau_phi

Paper and Citation

Paper: Prompt, Translate, Fine-Tune, Re-Initialize, or Instruction-Tune? Adapting LLMs for In-Context Learning in Low-Resource Languages

@misc{toukmaji2025prompttranslatefinetunereinitialize,
      title={Prompt, Translate, Fine-Tune, Re-Initialize, or Instruction-Tune? Adapting LLMs for In-Context Learning in Low-Resource Languages}, 
      author={Christopher Toukmaji and Jeffrey Flanigan},
      year={2025},
      eprint={2506.19187},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.19187}, 
}

laft_hau_phi

This model is a fine-tuned version of microsoft/phi-2 on the mc4 ha dataset. It achieves the following results on the evaluation set:

• Loss: 2.4573

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: 0.0003
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 2000
• num_epochs: 6.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.8519	1.0	24415	2.9492
1.7621	2.0	48830	2.8050
2.496	3.0	73245	2.6414
2.1467	4.0	97660	2.4562
2.8327	5.0	122075	2.3538
1.9926	6.0	146490	2.4573

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1