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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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metrics: |
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- accuracy |
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base_model: google/gemma-2b |
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model-index: |
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- name: RM-HH-Gemma_harmless_gpt3_20000_gemma2b_shuffleTrue_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-Gemma_harmless_gpt3_20000_gemma2b_shuffleTrue_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: 0.3493 |
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- Accuracy: 0.8350 |
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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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| 0.6993 | 0.03 | 250 | 0.6134 | 0.6557 | |
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| 0.5635 | 0.06 | 500 | 0.4914 | 0.7369 | |
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| 0.4753 | 0.08 | 750 | 0.4386 | 0.7647 | |
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| 0.4581 | 0.11 | 1000 | 0.4201 | 0.7794 | |
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| 0.4055 | 0.14 | 1250 | 0.4168 | 0.7879 | |
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| 0.4121 | 0.17 | 1500 | 0.4093 | 0.7922 | |
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| 0.388 | 0.19 | 1750 | 0.4091 | 0.7932 | |
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| 0.4249 | 0.22 | 2000 | 0.3978 | 0.8015 | |
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| 0.4087 | 0.25 | 2250 | 0.3929 | 0.8015 | |
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| 0.4016 | 0.28 | 2500 | 0.3915 | 0.8045 | |
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| 0.4309 | 0.31 | 2750 | 0.3702 | 0.8105 | |
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| 0.4258 | 0.33 | 3000 | 0.3625 | 0.8150 | |
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| 0.427 | 0.36 | 3250 | 0.3671 | 0.8137 | |
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| 0.3798 | 0.39 | 3500 | 0.3791 | 0.8132 | |
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| 0.3759 | 0.42 | 3750 | 0.3685 | 0.8152 | |
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| 0.4008 | 0.44 | 4000 | 0.3601 | 0.8192 | |
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| 0.3901 | 0.47 | 4250 | 0.3593 | 0.8220 | |
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| 0.3791 | 0.5 | 4500 | 0.3608 | 0.8235 | |
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| 0.3801 | 0.53 | 4750 | 0.3620 | 0.8225 | |
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| 0.3726 | 0.56 | 5000 | 0.3678 | 0.8225 | |
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| 0.4122 | 0.58 | 5250 | 0.3654 | 0.8220 | |
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| 0.363 | 0.61 | 5500 | 0.3647 | 0.8245 | |
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| 0.3808 | 0.64 | 5750 | 0.3569 | 0.8287 | |
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| 0.3977 | 0.67 | 6000 | 0.3534 | 0.8295 | |
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| 0.3492 | 0.69 | 6250 | 0.3551 | 0.8307 | |
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| 0.4155 | 0.72 | 6500 | 0.3462 | 0.8315 | |
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| 0.3879 | 0.75 | 6750 | 0.3485 | 0.8322 | |
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| 0.349 | 0.78 | 7000 | 0.3507 | 0.8312 | |
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| 0.4138 | 0.81 | 7250 | 0.3465 | 0.8352 | |
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| 0.3483 | 0.83 | 7500 | 0.3471 | 0.8350 | |
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| 0.3652 | 0.86 | 7750 | 0.3482 | 0.8355 | |
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| 0.3899 | 0.89 | 8000 | 0.3468 | 0.8345 | |
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| 0.3793 | 0.92 | 8250 | 0.3466 | 0.8352 | |
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| 0.3815 | 0.94 | 8500 | 0.3476 | 0.8352 | |
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| 0.3371 | 0.97 | 8750 | 0.3493 | 0.8350 | |
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### Framework versions |
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- PEFT 0.9.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
