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README.md
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---
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library_name: peft
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license: llama3
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base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
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tags:
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- axolotl
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- generated_from_trainer
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model-index:
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- name: e93d50ea-7420-4148-8bc1-2c4ecbdc2835
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<br>
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# e93d50ea-7420-4148-8bc1-2c4ecbdc2835
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This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3223
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.000212
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 120
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 8
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- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 50
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- training_steps: 500
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| No log | 0.0009 | 1 | 1.9441 |
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| 0.895 | 0.0456 | 50 | 0.9801 |
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| 0.7945 | 0.0912 | 100 | 0.8731 |
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| 0.6886 | 0.1369 | 150 | 0.8619 |
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| 0.7819 | 0.1825 | 200 | 0.7114 |
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| 0.6048 | 0.2281 | 250 | 0.5683 |
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| 0.503 | 0.2737 | 300 | 0.5383 |
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| 0.4986 | 0.3193 | 350 | 0.4003 |
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| 0.5244 | 0.3650 | 400 | 0.3518 |
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| 0.5655 | 0.4106 | 450 | 0.3261 |
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| 0.4691 | 0.4562 | 500 | 0.3223 |
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### Framework versions
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- PEFT 0.13.2
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- Transformers 4.46.0
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- Pytorch 2.5.0+cu124
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- Datasets 3.0.1
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- Tokenizers 0.20.1
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