---
license: apache-2.0
library_name: peft
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: zephyr-7b
  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. -->

# zephyr-7b

This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-qlora](https://huggingface.co/alignment-handbook/zephyr-7b-sft-qlora) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6928
- Rewards/chosen: -0.0289
- Rewards/rejected: -0.1011
- Rewards/accuracies: 0.3532
- Rewards/margins: 0.0722
- Logps/rejected: -85.5050
- Logps/chosen: -71.7912
- Logits/rejected: -2.1148
- Logits/chosen: -2.1436
- Use Label: 14417.4287
- Pred Label: 5654.5713

## 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: 5e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | Use Label  | Pred Label |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:----------:|:----------:|
| 0.6911        | 0.1   | 100  | 0.6919          | -0.0053        | -0.0356          | 0.3393             | 0.0303          | -78.9541       | -69.4262     | -2.0935         | -2.1210       | 1705.8572  | 150.1429   |
| 0.692         | 0.21  | 200  | 0.6927          | -0.0264        | -0.0695          | 0.3433             | 0.0431          | -82.3504       | -71.5409     | -2.1057         | -2.1268       | 3337.0476  | 622.9524   |
| 0.6924        | 0.31  | 300  | 0.6929          | -0.0369        | -0.0896          | 0.3393             | 0.0527          | -84.3537       | -72.5877     | -2.1933         | -2.2169       | 4863.7300  | 1200.2699  |
| 0.6927        | 0.42  | 400  | 0.6925          | -0.0211        | -0.0804          | 0.3413             | 0.0593          | -83.4364       | -71.0104     | -2.0934         | -2.1190       | 6324.0796  | 1843.9207  |
| 0.6924        | 0.52  | 500  | 0.6929          | -0.0206        | -0.0831          | 0.3433             | 0.0625          | -83.7112       | -70.9618     | -2.1518         | -2.1762       | 7772.7778  | 2499.2222  |
| 0.6929        | 0.63  | 600  | 0.6927          | -0.0452        | -0.1160          | 0.3512             | 0.0708          | -86.9945       | -73.4171     | -2.1125         | -2.1408       | 9198.8574  | 3177.1428  |
| 0.6928        | 0.73  | 700  | 0.6930          | -0.0507        | -0.1231          | 0.3512             | 0.0724          | -87.7077       | -73.9657     | -2.1086         | -2.1372       | 10627.2695 | 3852.7302  |
| 0.6927        | 0.84  | 800  | 0.6928          | -0.0272        | -0.0999          | 0.3552             | 0.0726          | -85.3832       | -71.6247     | -2.1141         | -2.1431       | 12045.5234 | 4538.4761  |
| 0.6929        | 0.94  | 900  | 0.6928          | -0.0288        | -0.1012          | 0.3492             | 0.0723          | -85.5160       | -71.7842     | -2.1139         | -2.1428       | 13461.3809 | 5226.6191  |


### Framework versions

- PEFT 0.7.1
- Transformers 4.38.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2