---
base_model: Minbyul/llama2-7b-wo-kqa_golden-sft
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: llama2-7b-dpo-full-sft-wo-kqa_golden
  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. -->

# llama2-7b-dpo-full-sft-wo-kqa_golden

This model is a fine-tuned version of [Minbyul/llama2-7b-wo-kqa_golden-sft](https://huggingface.co/Minbyul/llama2-7b-wo-kqa_golden-sft) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2778
- Rewards/chosen: -0.1016
- Rewards/rejected: -2.1516
- Rewards/accuracies: 0.9500
- Rewards/margins: 2.0501
- Logps/rejected: -771.6371
- Logps/chosen: -312.4064
- Logits/rejected: -0.5673
- Logits/chosen: -0.7867

## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.2497        | 0.74  | 100  | 0.3024          | -0.0879        | -1.9222          | 0.9500             | 1.8343          | -748.6945      | -311.0383    | -0.5637         | -0.7827       |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2