---
library_name: transformers
base_model: princeton-nlp/Llama-3-Base-8B-SFT
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: llama-3-8b-dpo-ultrafeedback-5e-7-SFTed-paged_adamw_32bit-increase_linear-0.95to1.0
  results: []
---

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# llama-3-8b-dpo-ultrafeedback-5e-7-SFTed-paged_adamw_32bit-increase_linear-0.95to1.0

This is a model released from the preprint: [DPO-Shift: Shifting the Distribution of Direct Preference Optimization](https://arxiv.org/abs/2502.07599). Please refer to our [repository](https://github.com/Meaquadddd/DPO-Shift) for more details.


This model is a fine-tuned version of [princeton-nlp/Llama-3-Base-8B-SFT](https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5521
- Rewards/chosen: -0.4441
- Rewards/rejected: -0.9724
- Dpo Lambda: 0.9972
- Rewards/accuracies: 0.7330
- Rewards/margins: 0.5282
- Logps/rejected: -368.2690
- Logps/chosen: -345.0610
- Logits/rejected: -1.0526
- Logits/chosen: -1.0164

## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 4
- 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 | Dpo Lambda | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:----------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6826        | 0.1047 | 50   | 0.6800          | 0.1038         | 0.0794           | 0.9552     | 0.6390             | 0.0244          | -263.0930      | -290.2701    | -0.9133         | -0.8451       |
| 0.6082        | 0.2094 | 100  | 0.6337          | -0.0205        | -0.1780          | 0.9605     | 0.7020             | 0.1574          | -288.8296      | -302.7013    | -0.9937         | -0.9367       |
| 0.6284        | 0.3141 | 150  | 0.6010          | -0.1351        | -0.4143          | 0.9657     | 0.7100             | 0.2792          | -312.4618      | -314.1588    | -0.9587         | -0.9129       |
| 0.6285        | 0.4187 | 200  | 0.5842          | -0.2139        | -0.5788          | 0.9710     | 0.7190             | 0.3649          | -328.9060      | -322.0359    | -0.9686         | -0.9279       |
| 0.5768        | 0.5234 | 250  | 0.5719          | -0.3843        | -0.8383          | 0.9762     | 0.7170             | 0.4540          | -354.8630      | -339.0806    | -1.0255         | -0.9855       |
| 0.5425        | 0.6281 | 300  | 0.5668          | -0.3998        | -0.8767          | 0.9814     | 0.7220             | 0.4769          | -358.6997      | -340.6255    | -1.0275         | -0.9893       |
| 0.573         | 0.7328 | 350  | 0.5578          | -0.4303        | -0.9403          | 0.9867     | 0.7280             | 0.5101          | -365.0644      | -343.6735    | -1.0352         | -0.9987       |
| 0.5364        | 0.8375 | 400  | 0.5543          | -0.4206        | -0.9426          | 0.9919     | 0.7320             | 0.5220          | -365.2877      | -342.7060    | -1.0446         | -1.0087       |
| 0.5385        | 0.9422 | 450  | 0.5521          | -0.4441        | -0.9724          | 0.9972     | 0.7330             | 0.5282          | -368.2690      | -345.0610    | -1.0526         | -1.0164       |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1