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--- |
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library_name: peft |
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license: mit |
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base_model: microsoft/phi-2 |
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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: 15d56899-41e5-46be-9482-e05c51fc9787 |
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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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# 15d56899-41e5-46be-9482-e05c51fc9787 |
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This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6424 |
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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: 0.000214 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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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 | 0.0000 | 1 | 0.7124 | |
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| 0.7606 | 0.0021 | 50 | 0.7107 | |
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| 0.7933 | 0.0042 | 100 | 0.8085 | |
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| 0.8646 | 0.0063 | 150 | 0.8180 | |
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| 0.8118 | 0.0084 | 200 | 0.6951 | |
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| 0.7386 | 0.0105 | 250 | 0.6735 | |
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| 0.7899 | 0.0126 | 300 | 0.6701 | |
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| 0.7901 | 0.0148 | 350 | 0.6612 | |
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| 0.7539 | 0.0169 | 400 | 0.6434 | |
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| 0.7689 | 0.0190 | 450 | 0.6437 | |
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| 0.737 | 0.0211 | 500 | 0.6424 | |
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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 |
