---
license: mit
library_name: peft
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
base_model: HuggingFaceH4/mistral-7b-sft-beta
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: mistral-sft-7b-dpo-qlora
  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-sft-7b-dpo-qlora

This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6936
- Rewards/chosen: 0.0005
- Rewards/rejected: 0.0001
- Rewards/accuracies: 0.6875
- Rewards/margins: 0.0003
- Logps/rejected: -122.9776
- Logps/chosen: -86.4464
- Logits/rejected: -3.0453
- Logits/chosen: -2.9824

## 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: 221
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results



### Framework versions

- PEFT 0.7.1
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.14.6
- Tokenizers 0.15.2