---
license: mit
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: microsoft/phi-2
model-index:
- name: phi2-lora-distilabel-intel-orca-dpo-pairs
  results: []
---
*Note from me: This is a fine tuned Phi-2 on Argilla-provided Intel Orca DPO pairs. It's run with the default settings, just with the batch sized at 2 instead of 1. The below was automatically generated by the trainer. It cost about $2.50 to train on RunPod.*
<!-- 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. -->

# phi2-lora-distilabel-intel-orca-dpo-pairs

This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4467
- Rewards/chosen: -0.0981
- Rewards/rejected: -1.3106
- Rewards/accuracies: 0.8410
- Rewards/margins: 1.2125
- Logps/rejected: -228.4777
- Logps/chosen: -209.0628
- Logits/rejected: 0.4528
- Logits/chosen: 0.2946

## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.5578        | 0.78  | 250  | 0.4467          | -0.0981        | -1.3106          | 0.8410             | 1.2125          | -228.4777      | -209.0628    | 0.4528          | 0.2946        |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2