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--- |
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language: |
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- en |
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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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- GEM/viggo |
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base_model: microsoft/phi-2 |
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model-index: |
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- name: phi-2 |
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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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# phi-2 |
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This model is a fine-tuned version of [microsoftl](https://huggingface.co/microsoftl) on the GEM/viggo dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2330 |
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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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| 1.917 | 0.04 | 50 | 1.4649 | |
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| 0.7037 | 0.08 | 100 | 0.4905 | |
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| 0.4209 | 0.12 | 150 | 0.3564 | |
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| 0.3534 | 0.16 | 200 | 0.3127 | |
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| 0.311 | 0.2 | 250 | 0.2940 | |
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| 0.2944 | 0.24 | 300 | 0.2798 | |
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| 0.2838 | 0.27 | 350 | 0.2710 | |
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| 0.2744 | 0.31 | 400 | 0.2634 | |
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| 0.2657 | 0.35 | 450 | 0.2577 | |
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| 0.2692 | 0.39 | 500 | 0.2513 | |
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| 0.263 | 0.43 | 550 | 0.2475 | |
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| 0.2664 | 0.47 | 600 | 0.2451 | |
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| 0.2535 | 0.51 | 650 | 0.2421 | |
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| 0.2594 | 0.55 | 700 | 0.2396 | |
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| 0.234 | 0.59 | 750 | 0.2379 | |
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| 0.2383 | 0.63 | 800 | 0.2361 | |
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| 0.2419 | 0.67 | 850 | 0.2350 | |
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| 0.2448 | 0.71 | 900 | 0.2337 | |
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| 0.241 | 0.74 | 950 | 0.2332 | |
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| 0.219 | 0.78 | 1000 | 0.2330 | |
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### Framework versions |
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- PEFT 0.7.2.dev0 |
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- Transformers 4.37.0.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 |
