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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: 0.7503 |
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- Rewards/chosen: -4.2523 |
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- Rewards/rejected: -4.3731 |
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- Rewards/accuracies: 0.5625 |
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- Rewards/margins: 0.1208 |
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- Logps/rejected: -168.5040 |
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- Logps/chosen: -147.3926 |
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- Logits/rejected: -11.6528 |
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- Logits/chosen: -11.5062 |
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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: 0.0001 |
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- train_batch_size: 16 |
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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: 4050 |
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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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| 0.8022 | 0.2 | 2000 | 0.7737 | -4.8718 | -5.0523 | 0.5724 | 0.1805 | -175.2956 | -153.5872 | -11.7730 | -11.6673 | |
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| 0.7336 | 0.4 | 4000 | 0.7503 | -4.2523 | -4.3731 | 0.5625 | 0.1208 | -168.5040 | -147.3926 | -11.6528 | -11.5062 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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