


# KULLM-Polyglot-12.8B-v2

This model is a fine-tuned version of [EleutherAI/polyglot-ko-12.8b](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) on a KULLM v2 

Detail Codes are available at [KULLM Github Repository](https://github.com/nlpai-lab/KULLM)


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

- learning_rate: 5e-05
- train_batch_size: 64
- seed: 42
- distributed_type: multi-GPU (A100 80G)
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10.0

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
{"language": ["ko"], "license": "mit", "tags": ["kullm-13.b", "polyglot-ko", "gpt-neox"], "pipeline_tag": "text-generation"}
transformers pytorch gpt_neox feature-extraction kullm-13.b polyglot-ko gpt-neox text-generation ko license:mit endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #gpt_neox #feature-extraction #kullm-13.b #polyglot-ko #gpt-neox #text-generation #ko #license-mit #endpoints_compatible #has_space #text-generation-inference #region-us 




# KULLM-Polyglot-12.8B-v2

This model is a fine-tuned version of EleutherAI/polyglot-ko-12.8b on a KULLM v2 

Detail Codes are available at KULLM Github Repository


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

- learning_rate: 5e-05
- train_batch_size: 64
- seed: 42
- distributed_type: multi-GPU (A100 80G)
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10.0

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
# KULLM-Polyglot-12.8B-v2

This model is a fine-tuned version of EleutherAI/polyglot-ko-12.8b on a KULLM v2 

Detail Codes are available at KULLM Github Repository ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:

- learning_rate: 5e-05
- train_batch_size: 64
- seed: 42
- distributed_type: multi-GPU (A100 80G)
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10.0 ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #gpt_neox #feature-extraction #kullm-13.b #polyglot-ko #gpt-neox #text-generation #ko #license-mit #endpoints_compatible #has_space #text-generation-inference #region-us 
 # KULLM-Polyglot-12.8B-v2

This model is a fine-tuned version of EleutherAI/polyglot-ko-12.8b on a KULLM v2 

Detail Codes are available at KULLM Github Repository ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:

- learning_rate: 5e-05
- train_batch_size: 64
- seed: 42
- distributed_type: multi-GPU (A100 80G)
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10.0 ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3