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---
license: mit
base_model: openai-community/gpt2
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: mistral_test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_test
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rewards/chosen: nan
- Rewards/rejected: nan
- Rewards/accuracies: 0.0
- Rewards/margins: nan
- Logps/rejected: nan
- Logps/chosen: nan
- Logits/rejected: nan
- Logits/chosen: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.0 | 0.0 | 10 | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan |
| 0.0 | 0.0 | 20 | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan |
| 0.0 | 0.0 | 30 | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan |
| 0.0 | 0.0 | 40 | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan |
| 0.0 | 0.0 | 50 | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan |
### Framework versions
- Transformers 4.37.2
- Pytorch 1.11.0+cu113
- Datasets 2.17.0
- Tokenizers 0.15.1
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