---
library_name: transformers
base_model: llama-3-8b-base-sft-hh-harmless-4xh200-batch-64
tags:
- alignment-handbook
- epsilon-dpo
- generated_from_trainer
datasets:
- Anthropic/hh-rlhf
model-index:
- name: llama-3-8b-base-epsilon-dpo-hh-harmless-4xh200-batch-64-20260418-003215
  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. -->

# llama-3-8b-base-epsilon-dpo-hh-harmless-4xh200-batch-64-20260418-003215

This model is a fine-tuned version of [llama-3-8b-base-sft-hh-harmless-4xh200-batch-64](https://huggingface.co/llama-3-8b-base-sft-hh-harmless-4xh200-batch-64) on the Anthropic/hh-rlhf dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5778
- Epsilon Dpo/beta: 0.0075
- Epsilon Dpo/loss Margin Mean: 44.6202
- Epsilon Dpo/beta Margin Mean: 0.3297
- Epsilon Dpo/beta Margin Std: 0.5411
- Epsilon Dpo/beta Margin Grad Mean: -0.4237
- Epsilon Dpo/beta Margin Grad Std: 0.1239
- Rewards/chosen: -0.6980
- Rewards/rejected: -1.0277
- Rewards/accuracies: 0.7192
- Rewards/margins: 0.3297
- Logps/chosen: -168.1115
- Logps/rejected: -217.4212
- Logps/ref Chosen: -74.8595
- Logps/ref Rejected: -79.5490
- Logits/chosen: 0.0396
- Logits/rejected: -0.0641
- Kl/p Epsilon Steps: 0.7196
- Kl/n Epsilon Steps: 0.2799

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Epsilon Dpo/beta | Epsilon Dpo/loss Margin Mean | Epsilon Dpo/beta Margin Mean | Epsilon Dpo/beta Margin Std | Epsilon Dpo/beta Margin Grad Mean | Epsilon Dpo/beta Margin Grad Std | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/chosen | Logps/rejected | Logps/ref Chosen | Logps/ref Rejected | Logits/chosen | Logits/rejected | Kl/p Epsilon Steps | Kl/n Epsilon Steps |
|:-------------:|:------:|:----:|:---------------:|:----------------:|:----------------------------:|:----------------------------:|:---------------------------:|:---------------------------------:|:--------------------------------:|:--------------:|:----------------:|:------------------:|:---------------:|:------------:|:--------------:|:----------------:|:------------------:|:-------------:|:---------------:|:------------------:|:------------------:|
| 1.3305        | 0.1512 | 100  | 0.6591          | 0.0821           | 1.4524                       | 0.1163                       | 0.4195                      | -0.4725                           | 0.0969                           | -0.4073        | -0.5236          | 0.6298             | 0.1163          | -79.8024     | -85.9443       | -74.8595         | -79.5490           | -0.2133       | -0.2955         | 0.6109             | 0.3882             |
| 0.9619        | 0.3023 | 200  | 0.5464          | 0.0537           | 12.5090                      | 0.6642                       | 1.1185                      | -0.3726                           | 0.2095                           | -1.0244        | -1.6886          | 0.7183             | 0.6642          | -93.8833     | -111.0817      | -74.8595         | -79.5490           | -0.1899       | -0.3034         | 0.7240             | 0.2751             |
| 1.0235        | 0.4535 | 300  | 0.5323          | 0.0324           | 22.9568                      | 0.7358                       | 1.1722                      | -0.3618                           | 0.2142                           | -1.6265        | -2.3623          | 0.7284             | 0.7358          | -124.9662    | -152.6126      | -74.8595         | -79.5490           | -0.0481       | -0.1742         | 0.7293             | 0.2698             |
| 1.132         | 0.6047 | 400  | 0.5402          | 0.0198           | 30.5708                      | 0.5994                       | 0.9731                      | -0.3799                           | 0.1895                           | -1.2023        | -1.8017          | 0.7302             | 0.5994          | -135.4181    | -170.6785      | -74.8595         | -79.5490           | -0.0432       | -0.1614         | 0.7293             | 0.2698             |
| 1.0834        | 0.7559 | 500  | 0.5507          | 0.0121           | 40.9401                      | 0.4894                       | 0.7940                      | -0.3952                           | 0.1670                           | -1.0111        | -1.5005          | 0.7210             | 0.4894          | -158.3591    | -203.9887      | -74.8595         | -79.5490           | 0.0215        | -0.0872         | 0.7227             | 0.2764             |
| 1.1666        | 0.9070 | 600  | 0.5778          | 0.0075           | 44.6202                      | 0.3297                       | 0.5411                      | -0.4237                           | 0.1239                           | -0.6980        | -1.0277          | 0.7192             | 0.3297          | -168.1115    | -217.4212      | -74.8595         | -79.5490           | 0.0396        | -0.0641         | 0.7196             | 0.2799             |


### Framework versions

- Transformers 4.51.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.21.4