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lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3_Qwen_Qwen1.5-4B_5e-5_lora2

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lmind_nq_train6000_eval6489_v1_reciteonly_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_reciteonly_qa_v3
metrics:
  - accuracy
model-index:
  - name: lmind_nq_train6000_eval6489_v1_reciteonly_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_reciteonly_qa_v3
          type: tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5792914798206278
library_name: peft

lmind_nq_train6000_eval6489_v1_reciteonly_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_reciteonly_qa_v3 dataset. It achieves the following results on the evaluation set:

  • Loss: 2.7806
  • Accuracy: 0.5793

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
1.8147 0.9973 187 1.7073 0.6045
1.6968 2.0 375 1.6898 0.6062
1.6305 2.9973 562 1.6912 0.6071
1.5527 4.0 750 1.7046 0.6062
1.4642 4.9973 937 1.7309 0.6055
1.3868 6.0 1125 1.7706 0.6037
1.278 6.9973 1312 1.8113 0.6016
1.1816 8.0 1500 1.8514 0.6004
1.0781 8.9973 1687 1.9348 0.5973
1.0042 10.0 1875 1.9619 0.5969
0.9175 10.9973 2062 2.0437 0.5938
0.8259 12.0 2250 2.1340 0.5910
0.7665 12.9973 2437 2.1983 0.5899
0.6822 14.0 2625 2.2949 0.5864
0.6189 14.9973 2812 2.3464 0.5854
0.5588 16.0 3000 2.4582 0.5835
0.4806 16.9973 3187 2.5345 0.5829
0.4272 18.0 3375 2.6303 0.5805
0.3805 18.9973 3562 2.6586 0.5806
0.3421 19.9467 3740 2.7806 0.5793

Framework versions

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