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lmind_nq_train6000_eval6489_v1_doc_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2

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lmind_nq_train6000_eval6489_v1_doc_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2 / README.md
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metadata
license: other
base_model: Qwen/Qwen1.5-4B
tags:
  - generated_from_trainer
datasets:
  - tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
metrics:
  - accuracy
model-index:
  - name: lmind_nq_train6000_eval6489_v1_doc_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
          type: tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.560974358974359
library_name: peft

lmind_nq_train6000_eval6489_v1_doc_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2

This model is a fine-tuned version of Qwen/Qwen1.5-4B on the tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3 dataset. It achieves the following results on the evaluation set:

  • Loss: 2.2417
  • Accuracy: 0.5610

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: 0.0005
  • 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
1.8583 1.0 529 1.6376 0.5726
1.6329 2.0 1058 1.6881 0.5713
1.3464 3.0 1587 1.8256 0.5663
1.1624 4.0 2116 1.9223 0.5652
0.964 5.0 2645 1.9720 0.5643
0.8117 6.0 3174 2.0016 0.5647
0.7242 7.0 3703 2.0785 0.5639
0.6381 8.0 4232 2.0954 0.5645
0.573 9.0 4761 2.1067 0.5623
0.5269 10.0 5290 2.1356 0.5646
0.5144 11.0 5819 2.1951 0.5616
0.4887 12.0 6348 2.1779 0.5631
0.4636 13.0 6877 2.1757 0.5611
0.467 14.0 7406 2.1781 0.5624
0.4613 15.0 7935 2.2312 0.5612
0.4405 16.0 8464 2.1800 0.5629
0.4308 17.0 8993 2.1960 0.5628
0.4401 18.0 9522 2.2355 0.5610
0.4334 19.0 10051 2.2380 0.5608
0.4218 20.0 10580 2.2417 0.5610

Framework versions

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