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# falcon-mini-shakespeare

This model was trained from scratch on "tinyshakespeare" text file.

## Model description

The configuration and code is adapted from tiiuae/falcon-7b, with configuration parameters changed to make it a very tiny model.

- **License:** Apache 2.0.

## Intended uses & limitations

Intended just to aid debugging efforts of a GGML port of Falcon.

## Training and evaluation data

https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt

## Training procedure

Just used the single tinyshakespeare text file as both the training and validation set (split up into paragraphs). See:

https://colab.research.google.com/drive/1utyxwRbOHCxUCqkIeei2JDAlKvAleRaF?usp=sharing

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 10

### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "falcon-mini-shakespeare", "results": []}]}
transformers pytorch RefinedWebModel text-generation generated_from_trainer custom_code autotrain_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #RefinedWebModel #text-generation #generated_from_trainer #custom_code #autotrain_compatible #text-generation-inference #region-us 




# falcon-mini-shakespeare

This model was trained from scratch on "tinyshakespeare" text file.

## Model description

The configuration and code is adapted from tiiuae/falcon-7b, with configuration parameters changed to make it a very tiny model.

- License: Apache 2.0.

## Intended uses & limitations

Intended just to aid debugging efforts of a GGML port of Falcon.

## Training and evaluation data

URL

## Training procedure

Just used the single tinyshakespeare text file as both the training and validation set (split up into paragraphs). See:

URL

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 10

### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

# falcon-mini-shakespeare

This model was trained from scratch on "tinyshakespeare" text file. ## Model description

The configuration and code is adapted from tiiuae/falcon-7b, with configuration parameters changed to make it a very tiny model.

- License: Apache 2.0. ## Intended uses & limitations

Intended just to aid debugging efforts of a GGML port of Falcon. ## Training and evaluation data

URL ## Training procedure

Just used the single tinyshakespeare text file as both the training and validation set (split up into paragraphs). See:

URL ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 10 ### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #RefinedWebModel #text-generation #generated_from_trainer #custom_code #autotrain_compatible #text-generation-inference #region-us 
 # falcon-mini-shakespeare

This model was trained from scratch on "tinyshakespeare" text file. ## Model description

The configuration and code is adapted from tiiuae/falcon-7b, with configuration parameters changed to make it a very tiny model.

- License: Apache 2.0. ## Intended uses & limitations

Intended just to aid debugging efforts of a GGML port of Falcon. ## Training and evaluation data

URL ## Training procedure

Just used the single tinyshakespeare text file as both the training and validation set (split up into paragraphs). See:

URL ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 10 ### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3