---
library_name: transformers
tags:
- alignment-handbook
- epsilon-dpo
- generated_from_trainer
datasets:
- Anthropic/hh-rlhf
model-index:
- name: qwen3-8b-base-epsilon-dpo-hh-harmless-4xh200-batch-64-20260418-172841
  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. -->

# qwen3-8b-base-epsilon-dpo-hh-harmless-4xh200-batch-64-20260418-172841

This model is a fine-tuned version of `/scratch/qu.yang1/dynamic-dpo-v4/outputs/qwen3-8b-base-sft-hh-helpful-4xh200-batch-64-20260417-214452` on the Anthropic/hh-rlhf dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5753
- Epsilon Dpo/beta: 0.0120
- Epsilon Dpo/loss Margin Mean: 30.1703
- Epsilon Dpo/beta Margin Mean: 0.3587
- Epsilon Dpo/beta Margin Std: 0.6142
- Epsilon Dpo/beta Margin Grad Mean: -0.4187
- Epsilon Dpo/beta Margin Grad Std: 0.1376
- Rewards/chosen: -0.5348
- Rewards/rejected: -0.8935
- Rewards/accuracies: 0.7236
- Rewards/margins: 0.3587
- Logps/chosen: -131.7627
- Logps/rejected: -178.7413
- Logps/ref Chosen: -87.4272
- Logps/ref Rejected: -104.2355
- Logits/chosen: -0.2089
- Logits/rejected: -0.3424
- Kl/p Epsilon Steps: 0.7170
- Kl/n Epsilon Steps: 0.2817

## 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 |
|:-------------:|:------:|:----:|:---------------:|:----------------:|:----------------------------:|:----------------------------:|:---------------------------:|:---------------------------------:|:--------------------------------:|:--------------:|:----------------:|:------------------:|:---------------:|:------------:|:--------------:|:----------------:|:------------------:|:-------------:|:---------------:|:------------------:|:------------------:|
| 1.3499        | 0.1512 | 100  | 0.6692          | 0.0877           | 0.6693                       | 0.0574                       | 0.1823                      | -0.4858                           | 0.0449                           | -0.0275        | -0.0849          | 0.6312             | 0.0574          | -87.7332     | -105.2108      | -87.4272         | -104.2355          | 0.1122        | 0.1240          | 0.6241             | 0.3759             |
| 1.1283        | 0.3023 | 200  | 0.6028          | 0.0636           | 4.8746                       | 0.3057                       | 0.6498                      | -0.4313                           | 0.1445                           | -0.1604        | -0.4661          | 0.6831             | 0.3057          | -89.9090     | -111.5920      | -87.4272         | -104.2355          | -0.3390       | -0.3863         | 0.6734             | 0.3261             |
| 1.2376        | 0.4535 | 300  | 0.5699          | 0.0434           | 10.8082                      | 0.4637                       | 0.8475                      | -0.4021                           | 0.1759                           | -0.4198        | -0.8835          | 0.7086             | 0.4637          | -97.0338     | -124.6503      | -87.4272         | -104.2355          | -0.3510       | -0.4271         | 0.6941             | 0.3050             |
| 1.2438        | 0.6047 | 400  | 0.5624          | 0.0290           | 16.4413                      | 0.4712                       | 0.8253                      | -0.4003                           | 0.1706                           | -0.4065        | -0.8777          | 0.7179             | 0.4712          | -101.3740    | -134.6236      | -87.4272         | -104.2355          | -0.3535       | -0.4572         | 0.7064             | 0.2923             |
| 1.1147        | 0.7559 | 500  | 0.5588          | 0.0187           | 26.6232                      | 0.4919                       | 0.8457                      | -0.3960                           | 0.1755                           | -0.8059        | -1.2978          | 0.7205             | 0.4919          | -130.4120    | -173.8436      | -87.4272         | -104.2355          | -0.1937       | -0.3318         | 0.7157             | 0.2843             |
| 1.1925        | 0.9070 | 600  | 0.5753          | 0.0120           | 30.1703                      | 0.3587                       | 0.6142                      | -0.4187                           | 0.1376                           | -0.5348        | -0.8935          | 0.7236             | 0.3587          | -131.7627    | -178.7413      | -87.4272         | -104.2355          | -0.2089       | -0.3424         | 0.7170             | 0.2817             |


### Framework versions

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