---
library_name: transformers
base_model: llama-3-8b-base-sft-hh-helpful-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-helpful-4xh200-batch-64-20260418-001920
  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-helpful-4xh200-batch-64-20260418-001920

This model is a fine-tuned version of [llama-3-8b-base-sft-hh-helpful-4xh200-batch-64](https://huggingface.co/llama-3-8b-base-sft-hh-helpful-4xh200-batch-64) on the Anthropic/hh-rlhf dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5941
- Epsilon Dpo/beta: 0.0022
- Epsilon Dpo/loss Margin Mean: 124.0911
- Epsilon Dpo/beta Margin Mean: 0.2718
- Epsilon Dpo/beta Margin Std: 0.4719
- Epsilon Dpo/beta Margin Grad Mean: -0.4365
- Epsilon Dpo/beta Margin Grad Std: 0.1083
- Rewards/chosen: -0.9073
- Rewards/rejected: -1.1791
- Rewards/accuracies: 0.7183
- Rewards/margins: 0.2718
- Logps/chosen: -487.8939
- Logps/rejected: -619.7319
- Logps/ref Chosen: -79.0510
- Logps/ref Rejected: -86.7979
- Logits/chosen: 0.9628
- Logits/rejected: 1.3823
- Kl/p Epsilon Steps: 0.6956
- Kl/n Epsilon Steps: 0.3022

## 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 |
|:-------------:|:------:|:----:|:---------------:|:----------------:|:----------------------------:|:----------------------------:|:---------------------------:|:---------------------------------:|:--------------------------------:|:--------------:|:----------------:|:------------------:|:---------------:|:------------:|:--------------:|:----------------:|:------------------:|:-------------:|:---------------:|:------------------:|:------------------:|
| 0.9809        | 0.1468 | 100  | 0.5819          | 0.0612           | 10.0016                      | 0.6045                       | 1.2197                      | -0.3930                           | 0.2078                           | -0.7143        | -1.3188          | 0.6926             | 0.6045          | -90.6611     | -108.4097      | -79.0510         | -86.7979           | -0.9442       | -0.8901         | 0.6370             | 0.3617             |
| 0.7763        | 0.2937 | 200  | 0.5340          | 0.0331           | 25.3103                      | 0.8303                       | 1.4036                      | -0.3643                           | 0.2158                           | -1.1029        | -1.9332          | 0.7149             | 0.8303          | -112.2424    | -145.2996      | -79.0510         | -86.7979           | -0.9108       | -0.8737         | 0.6759             | 0.3232             |
| 0.7955        | 0.4405 | 300  | 0.4928          | 0.0169           | 54.8226                      | 0.9212                       | 1.3268                      | -0.3414                           | 0.2134                           | -1.7179        | -2.6391          | 0.7453             | 0.9212          | -180.2318    | -242.8013      | -79.0510         | -86.7979           | -0.4338       | -0.2913         | 0.7282             | 0.2710             |
| 0.6919        | 0.5874 | 400  | 0.5234          | 0.0084           | 77.8526                      | 0.6459                       | 0.9831                      | -0.3725                           | 0.1852                           | -1.3445        | -1.9904          | 0.7333             | 0.6459          | -239.2477    | -324.8472      | -79.0510         | -86.7979           | 0.0554        | 0.2763          | 0.7183             | 0.2808             |
| 0.9214        | 0.7342 | 500  | 0.5470          | 0.0042           | 108.2775                     | 0.4514                       | 0.6818                      | -0.4010                           | 0.1444                           | -1.4275        | -1.8789          | 0.7487             | 0.4514          | -417.7782    | -533.8026      | -79.0510         | -86.7979           | 0.5317        | 0.8735          | 0.7235             | 0.2753             |
| 1.0729        | 0.8811 | 600  | 0.5941          | 0.0022           | 124.0911                     | 0.2718                       | 0.4719                      | -0.4365                           | 0.1083                           | -0.9073        | -1.1791          | 0.7183             | 0.2718          | -487.8939    | -619.7319      | -79.0510         | -86.7979           | 0.9628        | 1.3823          | 0.6956             | 0.3022             |


### Framework versions

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