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--- |
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license: mit |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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datasets: |
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- viggo |
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base_model: microsoft/phi-2 |
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model-index: |
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- name: phi2-viggo-finetune |
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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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# phi2-viggo-finetune |
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This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the viggo dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2331 |
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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: 2.5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 5 |
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- training_steps: 1000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.9356 | 0.04 | 50 | 1.4822 | |
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| 0.7214 | 0.08 | 100 | 0.5014 | |
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| 0.4192 | 0.12 | 150 | 0.3561 | |
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| 0.3546 | 0.16 | 200 | 0.3135 | |
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| 0.3119 | 0.2 | 250 | 0.2935 | |
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| 0.2926 | 0.24 | 300 | 0.2799 | |
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| 0.283 | 0.27 | 350 | 0.2711 | |
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| 0.2731 | 0.31 | 400 | 0.2629 | |
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| 0.2637 | 0.35 | 450 | 0.2583 | |
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| 0.2693 | 0.39 | 500 | 0.2518 | |
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| 0.2634 | 0.43 | 550 | 0.2478 | |
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| 0.2652 | 0.47 | 600 | 0.2453 | |
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| 0.2514 | 0.51 | 650 | 0.2429 | |
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| 0.2588 | 0.55 | 700 | 0.2394 | |
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| 0.2321 | 0.59 | 750 | 0.2381 | |
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| 0.2348 | 0.63 | 800 | 0.2357 | |
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| 0.2414 | 0.67 | 850 | 0.2355 | |
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| 0.2455 | 0.71 | 900 | 0.2337 | |
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| 0.2442 | 0.74 | 950 | 0.2331 | |
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| 0.2192 | 0.78 | 1000 | 0.2331 | |
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### Framework versions |
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- PEFT 0.7.2.dev0 |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
