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--- |
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license: mit |
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base_model: EleutherAI/gpt-neo-125m |
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tags: |
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- trl |
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- dpo |
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- generated_from_trainer |
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model-index: |
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- name: gpt-neo-125m_hh_reward |
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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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# gpt-neo-125m_hh_reward |
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This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7764 |
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- Rewards/chosen: -1.0726 |
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- Rewards/rejected: -1.1588 |
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- Rewards/accuracies: 0.5592 |
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- Rewards/margins: 0.0861 |
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- Logps/rejected: -127.6699 |
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- Logps/chosen: -107.5507 |
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- Logits/rejected: -15.9221 |
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- Logits/chosen: -15.7406 |
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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: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 150 |
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- training_steps: 20050 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| |
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| 0.7685 | 0.1 | 2000 | 0.7585 | -0.6757 | -0.7643 | 0.5066 | 0.0886 | -126.6837 | -106.5583 | -16.3107 | -16.1234 | |
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| 0.795 | 0.2 | 4000 | 0.7531 | -0.9330 | -1.0793 | 0.5625 | 0.1463 | -127.4711 | -107.2016 | -16.6020 | -16.4468 | |
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| 0.7366 | 0.3 | 6000 | 0.7961 | -1.0751 | -1.1163 | 0.5033 | 0.0412 | -127.5638 | -107.5568 | -16.3735 | -16.2226 | |
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| 0.8288 | 0.4 | 8000 | 0.7860 | -1.0546 | -1.1457 | 0.5559 | 0.0911 | -127.6372 | -107.5056 | -16.4345 | -16.2588 | |
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| 0.7272 | 0.5 | 10000 | 0.7767 | -1.1017 | -1.2133 | 0.5658 | 0.1116 | -127.8062 | -107.6233 | -16.2325 | -16.0800 | |
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| 0.8404 | 0.6 | 12000 | 0.8029 | -1.2133 | -1.2948 | 0.5329 | 0.0815 | -128.0099 | -107.9024 | -15.9207 | -15.7572 | |
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| 0.8224 | 0.7 | 14000 | 0.7702 | -1.0578 | -1.1817 | 0.5625 | 0.1239 | -127.7272 | -107.5137 | -15.9578 | -15.7879 | |
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| 0.7267 | 0.8 | 16000 | 0.7929 | -1.1534 | -1.2255 | 0.5559 | 0.0721 | -127.8368 | -107.7526 | -16.0551 | -15.8740 | |
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| 0.6329 | 0.9 | 18000 | 0.8052 | -1.0222 | -1.0692 | 0.5428 | 0.0471 | -127.4461 | -107.4246 | -15.8842 | -15.7008 | |
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| 0.7247 | 1.0 | 20000 | 0.7764 | -1.0726 | -1.1588 | 0.5592 | 0.0861 | -127.6699 | -107.5507 | -15.9221 | -15.7406 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.15.0 |
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