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--- |
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license: gemma |
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library_name: peft |
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tags: |
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- trl |
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- reward-trainer |
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- generated_from_trainer |
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base_model: google/gemma-2b |
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metrics: |
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- accuracy |
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model-index: |
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- name: RM-HH-AllMix_harmless_gpt3_20000_gemma2b_shuffleFalse_extractchosenTrue |
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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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# RM-HH-AllMix_harmless_gpt3_20000_gemma2b_shuffleFalse_extractchosenTrue |
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This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1748 |
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- Accuracy: 0.9237 |
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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: 1.41e-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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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.7416 | 0.04 | 250 | 0.5877 | 0.7074 | |
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| 0.7004 | 0.08 | 500 | 0.4614 | 0.7980 | |
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| 0.6298 | 0.13 | 750 | 0.3453 | 0.8477 | |
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| 0.5979 | 0.17 | 1000 | 0.2723 | 0.8774 | |
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| 0.5842 | 0.21 | 1250 | 0.2469 | 0.8868 | |
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| 0.6257 | 0.25 | 1500 | 0.2255 | 0.8973 | |
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| 0.5833 | 0.29 | 1750 | 0.2103 | 0.9071 | |
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| 0.6368 | 0.33 | 2000 | 0.2061 | 0.9082 | |
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| 0.5854 | 0.38 | 2250 | 0.2063 | 0.9105 | |
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| 0.5458 | 0.42 | 2500 | 0.1990 | 0.9127 | |
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| 0.6079 | 0.46 | 2750 | 0.1993 | 0.9135 | |
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| 0.5819 | 0.5 | 3000 | 0.1917 | 0.9165 | |
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| 0.5823 | 0.54 | 3250 | 0.1844 | 0.9180 | |
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| 0.618 | 0.59 | 3500 | 0.1869 | 0.9188 | |
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| 0.6075 | 0.63 | 3750 | 0.1885 | 0.9169 | |
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| 0.5685 | 0.67 | 4000 | 0.1848 | 0.9191 | |
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| 0.5718 | 0.71 | 4250 | 0.1848 | 0.9206 | |
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| 0.5697 | 0.75 | 4500 | 0.1819 | 0.9210 | |
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| 0.5719 | 0.79 | 4750 | 0.1769 | 0.9229 | |
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| 0.5774 | 0.84 | 5000 | 0.1779 | 0.9218 | |
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| 0.5331 | 0.88 | 5250 | 0.1745 | 0.9233 | |
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| 0.564 | 0.92 | 5500 | 0.1752 | 0.9237 | |
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| 0.567 | 0.96 | 5750 | 0.1748 | 0.9237 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
