README.md

---
base_model: Minbyul/selfbiorag-7b-wo-healthsearch_qa-sft
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: selfbiorag-7b-dpo-full-sft-wo-healthsearch_qa
  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-sft-wo-healthsearch_qa

This model is a fine-tuned version of [Minbyul/selfbiorag-7b-wo-healthsearch_qa-sft](https://huggingface.co/Minbyul/selfbiorag-7b-wo-healthsearch_qa-sft) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4465
- Rewards/chosen: -0.5929
- Rewards/rejected: -1.6772
- Rewards/accuracies: 0.7846
- Rewards/margins: 1.0843
- Logps/rejected: -1480.8429
- Logps/chosen: -984.8102
- Logits/rejected: -3.4642
- Logits/chosen: -2.6475

## 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: 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



### Framework versions

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