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--- |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-v0.1 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: zephyr-7b-dpo-lora |
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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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# zephyr-7b-dpo-lora |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2082 |
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- Rewards/chosen: 1.3857 |
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- Rewards/rejected: -0.9066 |
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- Rewards/accuracies: 0.9414 |
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- Rewards/margins: 2.2923 |
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- Logps/rejected: -388.5903 |
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- Logps/chosen: -238.5479 |
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- Logits/rejected: -2.7219 |
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- Logits/chosen: -2.6178 |
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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: 5e-07 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 128 |
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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_ratio: 0.1 |
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- num_epochs: 1 |
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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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| 0.2019 | 1.0 | 1470 | 0.2082 | 1.3857 | -0.9066 | 0.9414 | 2.2923 | -388.5903 | -238.5479 | -2.7219 | -2.6178 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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