
# pygmalion-350m

This model is a fine-tuned version of [PygmalionAI/pygmalion-350m](https://huggingface.co/PygmalionAI/pygmalion-350m/) on a 2.4MB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2731
- Accuracy: 0.5187

## Model description

A proof-of-concept model based on PygmalionAI/pygmalion-350m, which was in turn based on OPT-350m.

This model was fine-tuned purely for testing purposes.

## Fine-tuning process

Fine-tuned on an A100-80GB with HF's `run_clm.py` script. It was run through 3 epochs with 8 batch size using 2.4MB dataset (split 75/25 between training and validation sets).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "pygmalion-350m", "results": []}]}
transformers pytorch safetensors opt text-generation generated_from_trainer autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# pygmalion-350m

This model is a fine-tuned version of PygmalionAI/pygmalion-350m on a 2.4MB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2731
- Accuracy: 0.5187

## Model description

A proof-of-concept model based on PygmalionAI/pygmalion-350m, which was in turn based on OPT-350m.

This model was fine-tuned purely for testing purposes.

## Fine-tuning process

Fine-tuned on an A100-80GB with HF's 'run_clm.py' script. It was run through 3 epochs with 8 batch size using 2.4MB dataset (split 75/25 between training and validation sets).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2

# pygmalion-350m

This model is a fine-tuned version of PygmalionAI/pygmalion-350m on a 2.4MB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2731
- Accuracy: 0.5187 ## Model description

A proof-of-concept model based on PygmalionAI/pygmalion-350m, which was in turn based on OPT-350m.

This model was fine-tuned purely for testing purposes. ## Fine-tuning process

Fine-tuned on an A100-80GB with HF's 'run_clm.py' script. It was run through 3 epochs with 8 batch size using 2.4MB dataset (split 75/25 between training and validation sets). ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # pygmalion-350m

This model is a fine-tuned version of PygmalionAI/pygmalion-350m on a 2.4MB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2731
- Accuracy: 0.5187 ## Model description

A proof-of-concept model based on PygmalionAI/pygmalion-350m, which was in turn based on OPT-350m.

This model was fine-tuned purely for testing purposes. ## Fine-tuning process

Fine-tuned on an A100-80GB with HF's 'run_clm.py' script. It was run through 3 epochs with 8 batch size using 2.4MB dataset (split 75/25 between training and validation sets). ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# pygmalion-350m

This model is a fine-tuned version of PygmalionAI/pygmalion-350m on a 2.4MB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2731
- Accuracy: 0.5187## Model description

A proof-of-concept model based on PygmalionAI/pygmalion-350m, which was in turn based on OPT-350m.

This model was fine-tuned purely for testing purposes.## Fine-tuning process

Fine-tuned on an A100-80GB with HF's 'run_clm.py' script. It was run through 3 epochs with 8 batch size using 2.4MB dataset (split 75/25 between training and validation sets).### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2