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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-Mix_harmless_gpt3_20000_gemma2b_shuffleFalse_extractchosenFalse |
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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-Mix_harmless_gpt3_20000_gemma2b_shuffleFalse_extractchosenFalse |
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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: 0.0445 |
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- Accuracy: 0.9815 |
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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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| 0.8191 | 0.06 | 250 | 0.5824 | 0.695 | |
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| 0.6294 | 0.11 | 500 | 0.1346 | 0.953 | |
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| 0.5811 | 0.17 | 750 | 0.0888 | 0.9705 | |
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| 0.5753 | 0.22 | 1000 | 0.0684 | 0.975 | |
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| 0.5539 | 0.28 | 1250 | 0.0588 | 0.979 | |
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| 0.5764 | 0.33 | 1500 | 0.0595 | 0.9785 | |
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| 0.5261 | 0.39 | 1750 | 0.0558 | 0.979 | |
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| 0.5423 | 0.44 | 2000 | 0.0533 | 0.9795 | |
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| 0.5261 | 0.5 | 2250 | 0.0501 | 0.98 | |
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| 0.5363 | 0.56 | 2500 | 0.0485 | 0.98 | |
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| 0.5051 | 0.61 | 2750 | 0.0472 | 0.981 | |
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| 0.5157 | 0.67 | 3000 | 0.0509 | 0.9795 | |
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| 0.5368 | 0.72 | 3250 | 0.0507 | 0.9785 | |
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| 0.5281 | 0.78 | 3500 | 0.0467 | 0.981 | |
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| 0.5005 | 0.83 | 3750 | 0.0450 | 0.9815 | |
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| 0.5239 | 0.89 | 4000 | 0.0445 | 0.9815 | |
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| 0.5111 | 0.94 | 4250 | 0.0445 | 0.9815 | |
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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 |
