peft tensorboard safetensors phi alignment-handbook generated_from_trainer trl dpo custom_code dataset:HuggingFaceH4/ultrafeedback_binarized base_model:microsoft/phi-2 license:mit region:us
---
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-ipo-ultrachat-lora
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# phi-2-ipo-ultrachat-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: 2499.1455
- Rewards/chosen: -0.0000
- Rewards/rejected: -0.0002
- Rewards/accuracies: 0.25
- Rewards/margins: 0.0002
- Logps/rejected: -94.2138
- Logps/chosen: -91.7291
- Logits/rejected: 0.8197
- Logits/chosen: 0.8014

## 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: 1

### Training results



### Framework versions

- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.14.6
- Tokenizers 0.15.2