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lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-5_lora2

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lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-5_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_recite_qa_v3
metrics:
  - accuracy
model-index:
  - name: lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-5_lora2
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
          type: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7753632286995515
library_name: peft

lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-5_lora2

This model is a fine-tuned version of Qwen/Qwen1.5-4B on the tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5804
  • Accuracy: 0.7754

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
1.8478 1.0 529 1.6699 0.6080
1.7862 2.0 1058 1.6003 0.6164
1.6531 3.0 1587 1.5363 0.6251
1.5515 4.0 2116 1.4608 0.6343
1.4038 5.0 2645 1.3876 0.6456
1.2751 6.0 3174 1.3186 0.6553
1.1475 7.0 3703 1.2514 0.6637
1.0282 8.0 4232 1.1740 0.676
0.9067 9.0 4761 1.1004 0.6870
0.8202 10.0 5290 1.0408 0.6964
0.7007 11.0 5819 0.9592 0.7084
0.6259 12.0 6348 0.8998 0.7191
0.553 13.0 6877 0.8332 0.7295
0.4948 14.0 7406 0.7799 0.7387
0.4221 15.0 7935 0.7330 0.7466
0.3911 16.0 8464 0.6805 0.7551
0.3377 17.0 8993 0.6475 0.7620
0.3179 18.0 9522 0.6195 0.7680
0.288 19.0 10051 0.5962 0.7723
0.2605 20.0 10580 0.5804 0.7754

Framework versions

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