peft tensorboard safetensors mistral alignment-handbook trl dpo generated_from_trainer dataset:HuggingFaceH4/ultrafeedback_binarized base_model:mistralai/Mistral-7B-v0.1 license:apache-2.0 region:us
---
license: apache-2.0
library_name: peft
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: zephyr-7b-dpo-qlora-fsdp
  results: []
---

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# zephyr-7b-dpo-qlora-fsdp

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: 0.6843
- Rewards/chosen: 0.0234
- Rewards/rejected: 0.0034
- Rewards/accuracies: 0.6211
- Rewards/margins: 0.0199
- Logps/rejected: -260.8430
- Logps/chosen: -258.9067
- Logits/rejected: -2.4164
- Logits/chosen: -2.4494

## 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: 15
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 480
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 0.1

### Training results



### Framework versions

- PEFT 0.9.0
- Transformers 4.38.1
- Pytorch 2.2.0+cu118
- Datasets 2.17.1
- Tokenizers 0.15.2