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lmind_hotpot_train8000_eval7405_v1_qa_1e-4_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_1e-4_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: 0.4897142857142857
library_name: peft

lmind_hotpot_train8000_eval7405_v1_qa_1e-4_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: 4.1528
  • Accuracy: 0.4897

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: 50.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2503 1.0 250 2.3237 0.5156
2.087 2.0 500 2.3309 0.5164
1.849 3.0 750 2.4019 0.5145
1.6193 4.0 1000 2.5039 0.5104
1.3666 5.0 1250 2.6544 0.5050
1.1435 6.0 1500 2.8436 0.5011
0.9171 7.0 1750 3.0320 0.4971
0.7531 8.0 2000 3.2585 0.4930
0.6101 9.0 2250 3.3418 0.4925
0.5392 10.0 2500 3.5373 0.4916
0.4718 11.0 2750 3.6313 0.4893
0.4446 12.0 3000 3.6736 0.4906
0.4204 13.0 3250 3.7342 0.4906
0.4131 14.0 3500 3.7778 0.4897
0.3924 15.0 3750 3.8210 0.4897
0.3913 16.0 4000 3.8833 0.4904
0.376 17.0 4250 3.8936 0.4898
0.3785 18.0 4500 3.8824 0.49
0.367 19.0 4750 3.9720 0.4901
0.3676 20.0 5000 3.9374 0.4909
0.3602 21.0 5250 3.9380 0.4904
0.3639 22.0 5500 3.9516 0.4910
0.3533 23.0 5750 4.0207 0.4916
0.3587 24.0 6000 3.9905 0.4917
0.3479 25.0 6250 4.0617 0.4915
0.3511 26.0 6500 4.0106 0.4903
0.3442 27.0 6750 4.0401 0.4910
0.3496 28.0 7000 4.0157 0.4897
0.34 29.0 7250 4.0503 0.4902
0.3448 30.0 7500 4.0786 0.4908
0.3406 31.0 7750 4.1239 0.4905
0.3375 32.0 8000 4.1210 0.4915
0.339 33.0 8250 4.1039 0.4898
0.3418 34.0 8500 4.0879 0.4902
0.3364 35.0 8750 4.0782 0.4907
0.3421 36.0 9000 4.0512 0.4910
0.3337 37.0 9250 4.1727 0.4895
0.3375 38.0 9500 4.1615 0.4889
0.3304 39.0 9750 4.1755 0.4899
0.3341 40.0 10000 4.1542 0.4903
0.3311 41.0 10250 4.1479 0.4889
0.3337 42.0 10500 4.1005 0.4907
0.3284 43.0 10750 4.1688 0.4909
0.3343 44.0 11000 4.1412 0.4904
0.3301 45.0 11250 4.0906 0.4917
0.3307 46.0 11500 4.1221 0.4895
0.328 47.0 11750 4.1250 0.4892
0.3293 48.0 12000 4.1082 0.4911
0.3261 49.0 12250 4.1219 0.4903
0.3279 50.0 12500 4.1528 0.4897

Framework versions

  • PEFT 0.5.0
  • Transformers 4.41.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1