---
license: mit
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
base_model: microsoft/phi-2
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: phi-2-gpo-renew2-b0.001-vllm-i1
  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. -->

# phi-2-gpo-renew2-b0.001-vllm-i1

This model is a fine-tuned version of [DUAL-GPO/phi-2-gpo-renew2-b0.001-i0](https://huggingface.co/DUAL-GPO/phi-2-gpo-renew2-b0.001-i0) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0674
- Rewards/chosen: 0.2141
- Rewards/rejected: 0.1823
- Rewards/accuracies: 0.5025
- Rewards/margins: 0.0317
- Logps/rejected: -1694.0739
- Logps/chosen: -2002.3224
- Logits/rejected: 0.1179
- Logits/chosen: 0.1215

## 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: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.1015        | 0.16  | 100  | 0.0587          | 0.1574         | 0.1353           | 0.5010             | 0.0221          | -1741.1520     | -2059.0308   | -0.0782         | -0.0740       |
| 0.1063        | 0.32  | 200  | 0.0662          | 0.2082         | 0.1782           | 0.5035             | 0.0300          | -1698.2368     | -2008.2380   | 0.0246          | 0.0308        |
| 0.089         | 0.48  | 300  | 0.0674          | 0.2080         | 0.1779           | 0.5025             | 0.0302          | -1698.5458     | -2008.3832   | 0.1068          | 0.1094        |
| 0.0836        | 0.64  | 400  | 0.0678          | 0.2130         | 0.1815           | 0.5045             | 0.0315          | -1694.8785     | -2003.3719   | 0.0938          | 0.0992        |
| 0.0823        | 0.8   | 500  | 0.0675          | 0.2147         | 0.1828           | 0.5030             | 0.0319          | -1693.5775     | -2001.7003   | 0.1134          | 0.1173        |
| 0.095         | 0.96  | 600  | 0.0674          | 0.2143         | 0.1825           | 0.5025             | 0.0318          | -1693.8877     | -2002.1124   | 0.1193          | 0.1230        |


### Framework versions

- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2