---
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: pythia-1.4b-dpo-full
  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. -->

# pythia-1.4b-dpo-full

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6403
- Rewards/chosen: 0.6094
- Rewards/rejected: 0.4102
- Rewards/accuracies: 0.5893
- Rewards/margins: 0.2002
- Logps/rejected: -2024.0
- Logps/chosen: -2320.0
- Logits/rejected: -0.6719
- Logits/chosen: -0.6172

## 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: 5
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 6
- total_train_batch_size: 30
- total_eval_batch_size: 48
- 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 | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.684         | 0.05  | 100  | 0.6768          | 0.2314         | 0.1904           | 0.4494             | 0.0405          | -2048.0        | -2352.0      | -0.7227         | -0.6641       |
| 0.663         | 0.1   | 200  | 0.6566          | 0.5977         | 0.4883           | 0.4940             | 0.1108          | -2016.0        | -2320.0      | -0.7266         | -0.6680       |
| 0.6529        | 0.15  | 300  | 0.6513          | 0.625          | 0.4941           | 0.5149             | 0.1279          | -2016.0        | -2320.0      | -0.7188         | -0.6562       |
| 0.6371        | 0.2   | 400  | 0.6491          | 0.6562         | 0.5              | 0.5595             | 0.1523          | -2016.0        | -2304.0      | -0.7266         | -0.6680       |
| 0.6206        | 0.25  | 500  | 0.6466          | 0.5391         | 0.3945           | 0.5952             | 0.1445          | -2024.0        | -2320.0      | -0.7148         | -0.6562       |
| 0.686         | 0.29  | 600  | 0.6446          | 0.5781         | 0.4180           | 0.5714             | 0.1592          | -2024.0        | -2320.0      | -0.7188         | -0.6602       |
| 0.6459        | 0.34  | 700  | 0.6449          | 0.5508         | 0.3633           | 0.6012             | 0.1885          | -2032.0        | -2320.0      | -0.6875         | -0.6289       |
| 0.6458        | 0.39  | 800  | 0.6421          | 0.5586         | 0.3867           | 0.5774             | 0.1709          | -2024.0        | -2320.0      | -0.6953         | -0.6406       |
| 0.6451        | 0.44  | 900  | 0.6398          | 0.7109         | 0.5039           | 0.5685             | 0.2070          | -2016.0        | -2304.0      | -0.6719         | -0.6133       |
| 0.6213        | 0.49  | 1000 | 0.6407          | 0.7734         | 0.5742           | 0.5714             | 0.2012          | -2008.0        | -2304.0      | -0.6602         | -0.6016       |
| 0.6313        | 0.54  | 1100 | 0.6387          | 0.5391         | 0.3555           | 0.5893             | 0.1807          | -2032.0        | -2320.0      | -0.6680         | -0.6094       |
| 0.6298        | 0.59  | 1200 | 0.6380          | 0.6953         | 0.4922           | 0.6042             | 0.2031          | -2016.0        | -2304.0      | -0.6523         | -0.5977       |
| 0.6461        | 0.64  | 1300 | 0.6396          | 0.5586         | 0.3613           | 0.5863             | 0.1963          | -2032.0        | -2320.0      | -0.6914         | -0.6367       |
| 0.6258        | 0.69  | 1400 | 0.6360          | 0.6914         | 0.4727           | 0.5923             | 0.2207          | -2016.0        | -2304.0      | -0.6758         | -0.6172       |
| 0.6347        | 0.74  | 1500 | 0.6375          | 0.625          | 0.4141           | 0.5893             | 0.2100          | -2024.0        | -2320.0      | -0.6641         | -0.6094       |
| 0.6185        | 0.79  | 1600 | 0.6382          | 0.5977         | 0.3926           | 0.6042             | 0.2051          | -2032.0        | -2320.0      | -0.6797         | -0.625        |
| 0.6408        | 0.83  | 1700 | 0.6374          | 0.5977         | 0.3926           | 0.5952             | 0.2041          | -2024.0        | -2320.0      | -0.6719         | -0.6172       |
| 0.662         | 0.88  | 1800 | 0.6355          | 0.6094         | 0.3984           | 0.6012             | 0.2119          | -2024.0        | -2320.0      | -0.6836         | -0.6289       |
| 0.6385        | 0.93  | 1900 | 0.6379          | 0.6055         | 0.3926           | 0.625              | 0.2129          | -2024.0        | -2320.0      | -0.6758         | -0.6211       |
| 0.6154        | 0.98  | 2000 | 0.6381          | 0.6094         | 0.4043           | 0.6012             | 0.2041          | -2024.0        | -2320.0      | -0.6758         | -0.6211       |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.14.6
- Tokenizers 0.15.2