gemma7_on_korean_conv
This model is a fine-tuned version of beomi/gemma-ko-7b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7037
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: 1
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 7200
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.8472 | 0.1281 | 200 | 0.8849 |
0.7799 | 0.2563 | 400 | 0.8185 |
0.7791 | 0.3844 | 600 | 0.7945 |
0.7505 | 0.5126 | 800 | 0.7877 |
0.7891 | 0.6407 | 1000 | 0.7616 |
0.689 | 0.7688 | 1200 | 0.7437 |
0.7612 | 0.8970 | 1400 | 0.7520 |
0.5183 | 1.0251 | 1600 | 0.8028 |
0.4562 | 1.1533 | 1800 | 0.7811 |
0.4584 | 1.2814 | 2000 | 0.7920 |
0.4535 | 1.4095 | 2200 | 0.7887 |
0.4268 | 1.5377 | 2400 | 0.8048 |
0.4368 | 1.6658 | 2600 | 0.7640 |
0.4435 | 1.7940 | 2800 | 0.7844 |
0.4327 | 1.9221 | 3000 | 0.7977 |
0.1711 | 2.0502 | 3200 | 1.0313 |
0.1856 | 2.1784 | 3400 | 0.9997 |
0.1812 | 2.3065 | 3600 | 0.9870 |
0.1876 | 2.4346 | 3800 | 0.9731 |
0.1927 | 2.5628 | 4000 | 0.9857 |
0.1964 | 2.6909 | 4200 | 1.0148 |
0.1948 | 2.8191 | 4400 | 1.0025 |
0.1865 | 2.9472 | 4600 | 1.0556 |
0.059 | 3.0753 | 4800 | 1.3127 |
0.0523 | 3.2035 | 5000 | 1.3947 |
0.0658 | 3.3316 | 5200 | 1.3980 |
0.0596 | 3.4598 | 5400 | 1.3785 |
0.0556 | 3.5879 | 5600 | 1.3936 |
0.0709 | 3.7160 | 5800 | 1.3858 |
0.0544 | 3.8442 | 6000 | 1.3943 |
0.0503 | 3.9723 | 6200 | 1.4319 |
0.0133 | 4.1005 | 6400 | 1.6485 |
0.0144 | 4.2286 | 6600 | 1.6932 |
0.0126 | 4.3567 | 6800 | 1.6980 |
0.0189 | 4.4849 | 7000 | 1.6962 |
0.0128 | 4.6130 | 7200 | 1.7037 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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