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# gptneo-1.3B-rm-harmless

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5449
- Accuracy: 0.7184
- Average Pos Score: 1.1621
- Average Neg Score: 0.4114
- Average Abs Score Diff: 1.1133

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Average Pos Score | Average Neg Score | Average Abs Score Diff |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:-----------------:|:----------------------:|
| 0.6274        | 0.15  | 200  | 0.5898          | 0.6968   | 1.7881            | 1.2979            | 0.8413                 |
| 0.5058        | 0.3   | 400  | 0.5811          | 0.7115   | 0.8394            | -0.0314           | 1.3799                 |
| 0.5699        | 0.45  | 600  | 0.5527          | 0.7167   | 1.2803            | 0.6006            | 1.0293                 |
| 0.5533        | 0.6   | 800  | 0.5542          | 0.7171   | 0.6689            | -0.1876           | 1.2930                 |
| 0.5396        | 0.75  | 1000 | 0.5444          | 0.7223   | 1.0977            | 0.3601            | 1.0977                 |
| 0.5791        | 0.9   | 1200 | 0.5449          | 0.7184   | 1.1621            | 0.4114            | 1.1133                 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "gptneo-1.3B-rm-harmless", "results": []}]}
transformers pytorch reward_model generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #reward_model #generated_from_trainer #endpoints_compatible #region-us 

gptneo-1.3B-rm-harmless
=======================


This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5449
* Accuracy: 0.7184
* Average Pos Score: 1.1621
* Average Neg Score: 0.4114
* Average Abs Score Diff: 1.1133


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: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #reward_model #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #reward_model #generated_from_trainer #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1+cu113
* Datasets 2.9.0
* Tokenizers 0.13.2