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---
license: mit
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
base_model: microsoft/phi-2
model-index:
- name: phi-2-dpo-ultrafeedback-lora
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-dpo-ultrafeedback-lora
This model is a fine-tuned version of [lole25/phi-2-sft-ultrachat-lora](https://huggingface.co/lole25/phi-2-sft-ultrachat-lora) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6537
- Rewards/chosen: -0.2570
- Rewards/rejected: -0.3767
- Rewards/accuracies: 0.6580
- Rewards/margins: 0.1196
- Logps/rejected: -269.1014
- Logps/chosen: -285.9487
- Logits/rejected: 0.7335
- Logits/chosen: 0.6309
## 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
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6929 | 0.21 | 100 | 0.6928 | 0.0002 | -0.0010 | 0.5320 | 0.0012 | -231.5360 | -260.2240 | 0.9168 | 0.8145 |
| 0.6893 | 0.42 | 200 | 0.6891 | -0.0038 | -0.0134 | 0.6500 | 0.0096 | -232.7742 | -260.6225 | 0.9234 | 0.8205 |
| 0.6809 | 0.63 | 300 | 0.6810 | -0.0312 | -0.0611 | 0.6680 | 0.0299 | -237.5431 | -263.3647 | 0.9151 | 0.8092 |
| 0.6671 | 0.84 | 400 | 0.6723 | -0.0854 | -0.1408 | 0.6640 | 0.0553 | -245.5124 | -268.7867 | 0.8790 | 0.7713 |
| 0.6627 | 1.05 | 500 | 0.6645 | -0.1494 | -0.2293 | 0.6680 | 0.0799 | -254.3704 | -275.1849 | 0.8294 | 0.7217 |
| 0.6476 | 1.26 | 600 | 0.6591 | -0.1979 | -0.2968 | 0.6640 | 0.0989 | -261.1124 | -280.0337 | 0.7883 | 0.6828 |
| 0.6488 | 1.47 | 700 | 0.6559 | -0.2310 | -0.3414 | 0.6620 | 0.1104 | -265.5783 | -283.3440 | 0.7549 | 0.6511 |
| 0.6449 | 1.67 | 800 | 0.6542 | -0.2518 | -0.3695 | 0.6560 | 0.1177 | -268.3814 | -285.4226 | 0.7372 | 0.6347 |
| 0.6487 | 1.88 | 900 | 0.6539 | -0.2571 | -0.3764 | 0.6560 | 0.1193 | -269.0724 | -285.9532 | 0.7320 | 0.6299 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.14.6
- Tokenizers 0.15.2 |