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lmind_nq_train6000_eval6489_v1_doc_qa_v3_Qwen_Qwen1.5-4B_3e-4_lora2

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lmind_nq_train6000_eval6489_v1_doc_qa_v3_Qwen_Qwen1.5-4B_3e-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_3e-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: 0.5640512820512821
library_name: peft

lmind_nq_train6000_eval6489_v1_doc_qa_v3_Qwen_Qwen1.5-4B_3e-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: 2.2532
  • Accuracy: 0.5641

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: 0.0003
  • 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
1.8369 1.0 529 1.6032 0.5751
1.6451 2.0 1058 1.6357 0.5746
1.3703 3.0 1587 1.7677 0.5716
1.1817 4.0 2116 1.8587 0.5718
0.9674 5.0 2645 1.9319 0.5713
0.7936 6.0 3174 1.9934 0.5704
0.67 7.0 3703 2.0467 0.5684
0.5604 8.0 4232 2.1218 0.5693
0.4747 9.0 4761 2.1342 0.5682
0.4191 10.0 5290 2.1679 0.5674
0.3971 11.0 5819 2.2081 0.5658
0.3753 12.0 6348 2.1840 0.5664
0.3571 13.0 6877 2.2324 0.5634
0.3526 14.0 7406 2.2190 0.5632
0.35 15.0 7935 2.2086 0.5639
0.3323 16.0 8464 2.2655 0.5654
0.3281 17.0 8993 2.2444 0.5667
0.3328 18.0 9522 2.2597 0.5626
0.3305 19.0 10051 2.2682 0.5633
0.3228 20.0 10580 2.2532 0.5641

Framework versions

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