
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# outputs

This model is a fine-tuned version of [bigscience/bloomz-3b](https://huggingface.co/bigscience/bloomz-3b) on the FourthBrainGenAI/MarketMail-AI dataset.

## Model description

bigscience/bloomz-3b

## Intended uses & limitations

This model is for demonstration purpose

## Training and evaluation data

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- training_steps: 100
- mixed_precision_training: Native AMP

### Training results

TrainOutput(global_step=100, training_loss=0.3372485587000847, metrics={'train_runtime': 522.2092, 'train_samples_per_second': 12.256, 'train_steps_per_second': 0.191, 'total_flos': 1.230954169199616e+16, 'train_loss': 0.3372485587000847, 'epoch': 100.0})

### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "bigscience-bloom-rail-1.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "outputs", "results": []}]}
generated_from_trainer license:bigscience-bloom-rail-1.0 region:us
TAGS
#generated_from_trainer #license-bigscience-bloom-rail-1.0 #region-us 




# outputs

This model is a fine-tuned version of bigscience/bloomz-3b on the FourthBrainGenAI/MarketMail-AI dataset.

## Model description

bigscience/bloomz-3b

## Intended uses & limitations

This model is for demonstration purpose

## Training and evaluation data

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- training_steps: 100
- mixed_precision_training: Native AMP

### Training results

TrainOutput(global_step=100, training_loss=0.3372485587000847, metrics={'train_runtime': 522.2092, 'train_samples_per_second': 12.256, 'train_steps_per_second': 0.191, 'total_flos': 1.230954169199616e+16, 'train_loss': 0.3372485587000847, 'epoch': 100.0})

### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

# outputs

This model is a fine-tuned version of bigscience/bloomz-3b on the FourthBrainGenAI/MarketMail-AI dataset. ## Model description

bigscience/bloomz-3b ## Intended uses & limitations

This model is for demonstration purpose ## Training and evaluation data ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- training_steps: 100
- mixed_precision_training: Native AMP ### Training results

TrainOutput(global_step=100, training_loss=0.3372485587000847, metrics={'train_runtime': 522.2092, 'train_samples_per_second': 12.256, 'train_steps_per_second': 0.191, 'total_flos': 1.230954169199616e+16, 'train_loss': 0.3372485587000847, 'epoch': 100.0}) ### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#generated_from_trainer #license-bigscience-bloom-rail-1.0 #region-us 
 # outputs

This model is a fine-tuned version of bigscience/bloomz-3b on the FourthBrainGenAI/MarketMail-AI dataset. ## Model description

bigscience/bloomz-3b ## Intended uses & limitations

This model is for demonstration purpose ## Training and evaluation data ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- training_steps: 100
- mixed_precision_training: Native AMP ### Training results

TrainOutput(global_step=100, training_loss=0.3372485587000847, metrics={'train_runtime': 522.2092, 'train_samples_per_second': 12.256, 'train_steps_per_second': 0.191, 'total_flos': 1.230954169199616e+16, 'train_loss': 0.3372485587000847, 'epoch': 100.0}) ### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3