---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: TheBloke/OpenHermes-2-Mistral-7B-GPTQ
model-index:
- name: mistral-dpo
  results: []
datasets:
- Dahoas/full-hh-rlhf
---

<!-- 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-dpo

This model is a fine-tuned version of [TheBloke/OpenHermes-2-Mistral-7B-GPTQ](https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GPTQ) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6930
- Rewards/chosen: -0.0558
- Rewards/rejected: -0.0669
- Rewards/accuracies: 0.5096
- Rewards/margins: 0.0111
- Logps/rejected: -200.9969
- Logps/chosen: -193.8321
- Logits/rejected: -2.2922
- Logits/chosen: -2.3906

## 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: 1
- eval_batch_size: 8
- 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: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6825        | 0.0   | 10   | 0.6930          | -0.0558        | -0.0669          | 0.5096             | 0.0111          | -200.9969      | -193.8321    | -2.2922         | -2.3906       |


### Framework versions

- PEFT 0.7.1
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0