peft tensorboard safetensors trl dpo generated_from_trainer base_model:mistralai/Mistral-7B-v0.1 license:apache-2.0 region:us
---
license: apache-2.0
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: mistral-dpo
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mistral-dpo

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.
It achieves the following results on the evaluation set:
- Loss: 0.5536
- Rewards/chosen: 0.2363
- Rewards/rejected: -0.3821
- Rewards/accuracies: 0.7095
- Rewards/margins: 0.6183
- Logps/rejected: -278.2731
- Logps/chosen: -292.0563
- Logits/rejected: -2.6610
- Logits/chosen: -2.7000

## 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: 2
- eval_batch_size: 4
- seed: 100
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.5093        | 1.0   | 625  | 0.5536          | 0.2363         | -0.3821          | 0.7095             | 0.6183          | -278.2731      | -292.0563    | -2.6610         | -2.7000       |


### Framework versions

- PEFT 0.7.2.dev0
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.1