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lmind_hotpot_train8000_eval7405_v1_qa_Qwen_Qwen1.5-4B_5e-5_lora2

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metadata
license: other
base_model: Qwen/Qwen1.5-4B
tags:
  - generated_from_trainer
datasets:
  - tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
metrics:
  - accuracy
model-index:
  - name: lmind_hotpot_train8000_eval7405_v1_qa_Qwen_Qwen1.5-4B_5e-5_lora2
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
          type: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.48784126984126985
library_name: peft

lmind_hotpot_train8000_eval7405_v1_qa_Qwen_Qwen1.5-4B_5e-5_lora2

This model is a fine-tuned version of Qwen/Qwen1.5-4B on the tyzhu/lmind_hotpot_train8000_eval7405_v1_qa dataset. It achieves the following results on the evaluation set:

  • Loss: 3.9023
  • Accuracy: 0.4878

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: 5e-05
  • train_batch_size: 1
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 8
  • 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: 20.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2742 1.0 250 2.3355 0.5148
2.1673 2.0 500 2.3223 0.5169
2.0189 3.0 750 2.3526 0.5157
1.8684 4.0 1000 2.4020 0.5140
1.7028 5.0 1250 2.4910 0.5100
1.5685 6.0 1500 2.5835 0.5072
1.4161 7.0 1750 2.6927 0.5036
1.2904 8.0 2000 2.8047 0.5009
1.1397 9.0 2250 2.9097 0.4979
1.0366 10.0 2500 3.0555 0.4951
0.9114 11.0 2750 3.1526 0.4928
0.8301 12.0 3000 3.2417 0.4928
0.7283 13.0 3250 3.3687 0.4894
0.6717 14.0 3500 3.5007 0.4899
0.6055 15.0 3750 3.5470 0.4894
0.5667 16.0 4000 3.6736 0.4876
0.5271 17.0 4250 3.7100 0.4870
0.5081 18.0 4500 3.7920 0.4877
0.4758 19.0 4750 3.8548 0.4868
0.4682 20.0 5000 3.9023 0.4878

Framework versions

  • PEFT 0.5.0
  • Transformers 4.40.2
  • Pytorch 2.3.0
  • Datasets 2.19.1
  • Tokenizers 0.19.1