---
library_name: transformers
base_model: mistral-7b-base-sft-hh-harmless-4xh200-batch-64-20260418-015332
tags:
- alignment-handbook
- epsilon-dpo
- generated_from_trainer
datasets:
- Anthropic/hh-rlhf
model-index:
- name: mistral-7b-base-epsilon-dpo-hh-harmless-4xh200-batch-64-20260418-015332
  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. -->

# mistral-7b-base-epsilon-dpo-hh-harmless-4xh200-batch-64-20260418-015332

This model is a fine-tuned version of [mistral-7b-base-sft-hh-harmless-4xh200-batch-64-20260418-015332](https://huggingface.co/mistral-7b-base-sft-hh-harmless-4xh200-batch-64-20260418-015332) on the Anthropic/hh-rlhf dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5935
- Epsilon Dpo/beta: 0.0044
- Epsilon Dpo/loss Margin Mean: 65.2805
- Epsilon Dpo/beta Margin Mean: 0.2810
- Epsilon Dpo/beta Margin Std: 0.5001
- Epsilon Dpo/beta Margin Grad Mean: -0.4338
- Epsilon Dpo/beta Margin Grad Std: 0.1154
- Rewards/chosen: -0.4127
- Rewards/rejected: -0.6936
- Rewards/accuracies: 0.7196
- Rewards/margins: 0.2810
- Logps/chosen: -171.7436
- Logps/rejected: -233.1436
- Logps/ref Chosen: -77.4087
- Logps/ref Rejected: -73.5282
- Logits/chosen: -3.1674
- Logits/rejected: -2.9705
- Kl/p Epsilon Steps: 0.7117
- Kl/n Epsilon Steps: 0.2870

## 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 |
|:-------------:|:------:|:----:|:---------------:|:----------------:|:----------------------------:|:----------------------------:|:---------------------------:|:---------------------------------:|:--------------------------------:|:--------------:|:----------------:|:------------------:|:---------------:|:------------:|:--------------:|:----------------:|:------------------:|:-------------:|:---------------:|:------------------:|:------------------:|
| 0.9822        | 0.1512 | 100  | 0.6127          | 0.0682           | 6.4966                       | 0.4365                       | 1.0172                      | -0.4126                           | 0.1971                           | -0.5724        | -1.0089          | 0.6646             | 0.4365          | -85.7551     | -88.3712       | -77.4087         | -73.5282           | -3.5599       | -3.5776         | 0.6721             | 0.3275             |
| 0.8262        | 0.3023 | 200  | 0.5655          | 0.0393           | 16.0515                      | 0.6241                       | 1.1422                      | -0.3816                           | 0.2119                           | -0.8102        | -1.4343          | 0.7007             | 0.6241          | -97.9143     | -110.0853      | -77.4087         | -73.5282           | -3.5815       | -3.5644         | 0.7073             | 0.2918             |
| 0.949         | 0.4535 | 300  | 0.5616          | 0.0224           | 23.5752                      | 0.5225                       | 0.9256                      | -0.3915                           | 0.1875                           | -0.6680        | -1.1905          | 0.7121             | 0.5225          | -107.0630    | -126.7577      | -77.4087         | -73.5282           | -3.5920       | -3.5350         | 0.7069             | 0.2918             |
| 0.9633        | 0.6047 | 400  | 0.5351          | 0.0130           | 47.5045                      | 0.6097                       | 0.9587                      | -0.3752                           | 0.1913                           | -0.7854        | -1.3951          | 0.7267             | 0.6097          | -137.7568    | -181.3808      | -77.4087         | -73.5282           | -3.4438       | -3.3090         | 0.7284             | 0.2698             |
| 1.0329        | 0.7559 | 500  | 0.5608          | 0.0073           | 59.4602                      | 0.4316                       | 0.7150                      | -0.4038                           | 0.1553                           | -0.5800        | -1.0116          | 0.7258             | 0.4316          | -156.1025    | -211.6822      | -77.4087         | -73.5282           | -3.2563       | -3.0824         | 0.7192             | 0.2790             |
| 1.0833        | 0.9070 | 600  | 0.5935          | 0.0044           | 65.2805                      | 0.2810                       | 0.5001                      | -0.4338                           | 0.1154                           | -0.4127        | -0.6936          | 0.7196             | 0.2810          | -171.7436    | -233.1436      | -77.4087         | -73.5282           | -3.1674       | -2.9705         | 0.7117             | 0.2870             |


### Framework versions

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