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README.md
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---
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library_name: peft
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base_model: katuni4ka/tiny-random-qwen1.5-moe
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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: 3e503c6c-35e3-4e36-97fd-3ca1b6490aa2
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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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# 3e503c6c-35e3-4e36-97fd-3ca1b6490aa2
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This model is a fine-tuned version of [katuni4ka/tiny-random-qwen1.5-moe](https://huggingface.co/katuni4ka/tiny-random-qwen1.5-moe) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 11.7886
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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 | 0.0002 | 1 | 11.9334 |
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| 11.8704 | 0.0082 | 50 | 11.8842 |
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| 11.8571 | 0.0163 | 100 | 11.8636 |
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| 11.8176 | 0.0245 | 150 | 11.8292 |
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| 11.8139 | 0.0327 | 200 | 11.8095 |
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| 11.8127 | 0.0408 | 250 | 11.7997 |
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| 11.8078 | 0.0490 | 300 | 11.7957 |
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| 11.7966 | 0.0572 | 350 | 11.7917 |
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| 11.795 | 0.0653 | 400 | 11.7895 |
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| 11.7874 | 0.0735 | 450 | 11.7888 |
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| 11.7957 | 0.0817 | 500 | 11.7886 |
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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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