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---
license: mit
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
base_model: microsoft/phi-2
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: phi-2-gpo-test-longest-iter-1
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-test-longest-iter-1
This model is a fine-tuned version of [DUAL-GPO/phi-2-gpo-test-longest-iter-0](https://huggingface.co/DUAL-GPO/phi-2-gpo-test-longest-iter-0) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0108
- Rewards/chosen: -0.0007
- Rewards/rejected: -0.0005
- Rewards/accuracies: 0.4995
- Rewards/margins: -0.0002
- Logps/rejected: -278.7133
- Logps/chosen: -306.4168
- Logits/rejected: 0.0867
- Logits/chosen: -0.0107
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.0103 | 1.6 | 100 | 0.0108 | -0.0015 | -0.0014 | 0.5010 | -0.0001 | -278.8064 | -306.4953 | 0.0771 | -0.0200 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 |