---
base_model: dmis-lab/selfbiorag_7b
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: selfbiorag-7b-dpo-full-wo-healthsearch_qa-ep3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# selfbiorag-7b-dpo-full-wo-healthsearch_qa-ep3

This model is a fine-tuned version of [dmis-lab/selfbiorag_7b](https://huggingface.co/dmis-lab/selfbiorag_7b) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6669
- Rewards/chosen: 0.0740
- Rewards/rejected: 0.0190
- Rewards/accuracies: 0.6977
- Rewards/margins: 0.0550
- Logps/rejected: -170.3629
- Logps/chosen: -158.5215
- Logits/rejected: -1.6515
- Logits/chosen: -1.6773

## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- 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 | Logits/chosen | Logits/rejected | Logps/chosen | Logps/rejected | Validation Loss | Rewards/accuracies | Rewards/chosen | Rewards/margins | Rewards/rejected |
|:-------------:|:-----:|:----:|:-------------:|:---------------:|:------------:|:--------------:|:---------------:|:------------------:|:--------------:|:---------------:|:----------------:|
| 0.6508        | 0.61  | 100  | -1.6968       | -1.6723         | -158.8701    | -170.2428      | 0.6691          | 0.6941             | 0.0706         | 0.0503          | 0.0202           |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2