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---
base_model: rtzr/ko-gemma-2-9b-it
library_name: peft
license: gemma
tags:
- generated_from_trainer
model-index:
- name: gemma9_on_korean_summary_events
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gemma9_on_korean_summary_events
This model is a fine-tuned version of [rtzr/ko-gemma-2-9b-it](https://huggingface.co/rtzr/ko-gemma-2-9b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4183
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5952 | 0.1316 | 20 | 1.0820 |
| 0.9103 | 0.2632 | 40 | 0.7513 |
| 0.7022 | 0.3947 | 60 | 0.5833 |
| 0.5149 | 0.5263 | 80 | 0.4630 |
| 0.4837 | 0.6579 | 100 | 0.4376 |
| 0.449 | 0.7895 | 120 | 0.4213 |
| 0.431 | 0.9211 | 140 | 0.4080 |
| 0.3811 | 1.0526 | 160 | 0.4000 |
| 0.3227 | 1.1842 | 180 | 0.3964 |
| 0.283 | 1.3158 | 200 | 0.3974 |
| 0.2984 | 1.4474 | 220 | 0.3993 |
| 0.3102 | 1.5789 | 240 | 0.3851 |
| 0.3045 | 1.7105 | 260 | 0.3847 |
| 0.3034 | 1.8421 | 280 | 0.3851 |
| 0.2779 | 1.9737 | 300 | 0.3793 |
| 0.2191 | 2.1053 | 320 | 0.3991 |
| 0.1971 | 2.2368 | 340 | 0.4157 |
| 0.1908 | 2.3684 | 360 | 0.4209 |
| 0.1766 | 2.5 | 380 | 0.4190 |
| 0.1749 | 2.6316 | 400 | 0.4183 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.43.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |