rishavranaut / llama2-7B_final_MT

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Model's Last Updated: October 22 2024

Introduction of llama2-7B_final_MT

Model Details of llama2-7B_final_MT

llama2-7B_final_MT

This model is a fine-tuned version of Qwen/Qwen2-7B on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5523
  • Accuracy: 0.8117
  • Precision: 0.7913
  • Recall: 0.8467
  • F1 score: 0.8180
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: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5
Training results
Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 score
0.8396 0.5 200 0.8733 0.645 0.6064 0.8267 0.6996
0.6541 1.0 400 0.6882 0.695 0.7127 0.6533 0.6817
0.4601 1.5 600 0.6691 0.7067 0.6505 0.8933 0.7528
0.4437 2.0 800 0.5010 0.7833 0.7690 0.81 0.7890
0.3406 2.5 1000 0.5010 0.7767 0.7823 0.7667 0.7744
0.2919 3.0 1200 0.4927 0.8117 0.8127 0.81 0.8114
0.2219 3.5 1400 0.4971 0.8217 0.8044 0.85 0.8266
0.2154 4.0 1600 0.6404 0.7633 0.7147 0.8767 0.7874
0.1381 4.5 1800 0.5391 0.815 0.8 0.84 0.8195
0.1531 5.0 2000 0.5523 0.8117 0.7913 0.8467 0.8180
Framework versions
  • PEFT 0.11.1
  • Transformers 4.44.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1

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https://huggingface.co/rishavranaut/llama2-7B_final_MT

llama2-7B_final_MT install

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