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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_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-Gemma_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: 0.0236 |
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- Accuracy: 0.9907 |
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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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| 0.5999 | 0.03 | 250 | 0.1410 | 0.9717 | |
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| 0.4197 | 0.06 | 500 | 0.0394 | 0.9882 | |
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| 0.4246 | 0.08 | 750 | 0.0347 | 0.9895 | |
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| 0.4616 | 0.11 | 1000 | 0.0345 | 0.9885 | |
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| 0.4141 | 0.14 | 1250 | 0.0308 | 0.9900 | |
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| 0.3989 | 0.17 | 1500 | 0.0311 | 0.9887 | |
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| 0.4122 | 0.19 | 1750 | 0.0299 | 0.9895 | |
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| 0.4106 | 0.22 | 2000 | 0.0298 | 0.9892 | |
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| 0.4657 | 0.25 | 2250 | 0.0270 | 0.9905 | |
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| 0.4311 | 0.28 | 2500 | 0.0304 | 0.9890 | |
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| 0.4474 | 0.31 | 2750 | 0.0277 | 0.9905 | |
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| 0.4202 | 0.33 | 3000 | 0.0293 | 0.9892 | |
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| 0.4487 | 0.36 | 3250 | 0.0287 | 0.9902 | |
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| 0.4219 | 0.39 | 3500 | 0.0257 | 0.9910 | |
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| 0.4525 | 0.42 | 3750 | 0.0264 | 0.9910 | |
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| 0.3805 | 0.44 | 4000 | 0.0277 | 0.9897 | |
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| 0.3824 | 0.47 | 4250 | 0.0241 | 0.9910 | |
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| 0.4217 | 0.5 | 4500 | 0.0235 | 0.9912 | |
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| 0.4275 | 0.53 | 4750 | 0.0259 | 0.9905 | |
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| 0.4395 | 0.56 | 5000 | 0.0247 | 0.9910 | |
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| 0.3848 | 0.58 | 5250 | 0.0250 | 0.9910 | |
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| 0.4297 | 0.61 | 5500 | 0.0249 | 0.9900 | |
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| 0.4167 | 0.64 | 5750 | 0.0258 | 0.9892 | |
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| 0.4205 | 0.67 | 6000 | 0.0244 | 0.9902 | |
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| 0.4072 | 0.69 | 6250 | 0.0264 | 0.9890 | |
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| 0.4033 | 0.72 | 6500 | 0.0253 | 0.9892 | |
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| 0.3699 | 0.75 | 6750 | 0.0244 | 0.9905 | |
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| 0.4101 | 0.78 | 7000 | 0.0259 | 0.9887 | |
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| 0.3969 | 0.81 | 7250 | 0.0249 | 0.9892 | |
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| 0.3845 | 0.83 | 7500 | 0.0236 | 0.9907 | |
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| 0.4208 | 0.86 | 7750 | 0.0232 | 0.9907 | |
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| 0.3925 | 0.89 | 8000 | 0.0232 | 0.9907 | |
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| 0.3769 | 0.92 | 8250 | 0.0231 | 0.9912 | |
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| 0.4323 | 0.94 | 8500 | 0.0232 | 0.9912 | |
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| 0.3999 | 0.97 | 8750 | 0.0236 | 0.9907 | |
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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 |
