ChrisToukmaji/focus_bur_phi_focus_trained

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}, 
}

focus_bur_phi_focus_trained

This model is a fine-tuned version of final_models/focus_bur_phi_after_focus_reinit on the mc4 my dataset. It achieves the following results on the evaluation set:

• Loss: 1.8202

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
1.5881	1.0	24415	2.1758
1.8	2.0	48830	2.0561
2.4986	3.0	73245	1.9480
0.9893	4.0	97660	1.8157
1.0487	5.0	122075	1.7515
0.8305	6.0	146490	1.8202

Framework versions

• Transformers 4.44.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.19.1