---
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-extra-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-extra-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: 0.0421
- Rewards/chosen: -0.0100
- Rewards/rejected: -0.0414
- Rewards/accuracies: 0.6015
- Rewards/margins: 0.0314
- Logps/rejected: -408.6569
- Logps/chosen: -406.3272
- Logits/rejected: -1.0065
- Logits/chosen: -1.0521

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.0982        | 0.11  | 100  | 0.0526          | -0.0081        | -0.0101          | 0.5190             | 0.0020          | -377.3773      | -404.4459    | -0.7816         | -0.8697       |
| 0.0846        | 0.21  | 200  | 0.0485          | -0.0265        | -0.0402          | 0.5530             | 0.0137          | -407.4654      | -422.8199    | -0.9792         | -1.0427       |
| 0.0859        | 0.32  | 300  | 0.0464          | -0.0257        | -0.0460          | 0.5725             | 0.0203          | -413.2813      | -422.0490    | -1.0612         | -1.1154       |
| 0.0957        | 0.43  | 400  | 0.0443          | -0.0207        | -0.0481          | 0.5780             | 0.0274          | -415.3487      | -417.0023    | -1.0450         | -1.0984       |
| 0.068         | 0.53  | 500  | 0.0432          | -0.0067        | -0.0318          | 0.5955             | 0.0252          | -399.0811      | -402.9732    | -0.9791         | -1.0329       |
| 0.0847        | 0.64  | 600  | 0.0427          | -0.0050        | -0.0312          | 0.5945             | 0.0263          | -398.4744      | -401.2879    | -0.9837         | -1.0364       |
| 0.0519        | 0.75  | 700  | 0.0423          | -0.0082        | -0.0377          | 0.5905             | 0.0295          | -404.9791      | -404.5331    | -0.9872         | -1.0360       |
| 0.0742        | 0.85  | 800  | 0.0422          | -0.0105        | -0.0420          | 0.6000             | 0.0315          | -409.2462      | -406.8035    | -1.0109         | -1.0556       |
| 0.0768        | 0.96  | 900  | 0.0421          | -0.0100        | -0.0415          | 0.5930             | 0.0315          | -408.7397      | -406.3475    | -1.0050         | -1.0502       |


### Framework versions

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