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lmind_hotpot_train8000_eval7405_v1_docidx_meta-llama_Llama-2-7b-hf_lora2

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7676
  • Accuracy: 0.7929

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: 0.0001
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.108 1.0 839 1.3252 0.7537
1.0489 2.0 1678 1.2690 0.7580
0.9812 3.0 2517 1.1602 0.7631
0.902 4.0 3357 1.0981 0.7679
0.8047 5.0 4196 1.0106 0.7727
0.7028 6.0 5035 0.9448 0.7777
0.6141 7.0 5874 0.8789 0.7818
0.5393 8.0 6714 0.8737 0.7859
0.4595 9.0 7553 0.8019 0.7896
0.3927 10.0 8390 0.7676 0.7929

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.14.1
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Dataset used to train tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx_meta-llama_Llama-2-7b-hf_lora2

Evaluation results

  • Accuracy on tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx
    self-reported
    0.793