The model was trained on lyrics from Drake.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/drake).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/drake")
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/drake")

model = AutoModelWithLMHead.from_pretrained("huggingartists/drake")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1ba7t9q0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Drake's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/18yhidb2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/18yhidb2/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/drake')
generator("I am", num_return_sequences=5)
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: drake


model author: huggingartists


task: text-generation


tags: pytorch, gpt2, text-generation, en, dataset:huggingartists/drake, transformers, huggingartists, lyrics


language: en
datasets:


- huggingartists/mumiy-troll
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Мумий Тролль (Mumiy Troll)

@mumiy-troll


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Мумий Тролль (Mumiy Troll).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/mumiy-troll).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/mumiy-troll")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/8o66pyeu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Мумий Тролль (Mumiy Troll)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/32hmbbel) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/32hmbbel/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/mumiy-troll')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/mumiy-troll")

model = AutoModelWithLMHead.from_pretrained("huggingartists/mumiy-troll")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: mumiy-troll


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/mumiy-troll, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/mc-ride
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

MC Ride

@mc-ride


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from MC Ride.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/mc-ride).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/mc-ride")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2ar7kgj5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on MC Ride's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/299iw75q) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/299iw75q/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/mc-ride')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/mc-ride")

model = AutoModelWithLMHead.from_pretrained("huggingartists/mc-ride")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: mc-ride


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/mc-ride, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/joji
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Joji

@joji


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Joji.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/joji).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/joji")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/ns61e8zi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Joji's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/jz3ft48t) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/jz3ft48t/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/joji')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/joji")

model = AutoModelWithLMHead.from_pretrained("huggingartists/joji")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: joji


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/joji, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/adele
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Adele

@adele


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Adele.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/adele).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/adele")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1yyqw6ss/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Adele's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3qruwjpr) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3qruwjpr/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/adele')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/adele")

model = AutoModelWithLMHead.from_pretrained("huggingartists/adele")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: adele


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/adele, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/pharaoh
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

PHARAOH

@pharaoh


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from PHARAOH.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/pharaoh).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/pharaoh")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/jefxst5w/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on PHARAOH's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1fqlqxjo) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1fqlqxjo/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/pharaoh')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/pharaoh")

model = AutoModelWithLMHead.from_pretrained("huggingartists/pharaoh")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: pharaoh


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/pharaoh, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/billie-eilish
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Billie Eilish

@billie-eilish


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Billie Eilish.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/billie-eilish).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/billie-eilish")
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/billie-eilish")

model = AutoModelWithLMHead.from_pretrained("huggingartists/billie-eilish")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3l1r2mnu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Billie Eilish's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/209kskmi) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/209kskmi/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/billie-eilish')
generator("I am", num_return_sequences=5)
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: billie-eilish


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/billie-eilish, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/viktor-tsoi
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Виктор Цой (Viktor Tsoi)

@viktor-tsoi


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Виктор Цой (Viktor Tsoi).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/viktor-tsoi).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/viktor-tsoi")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1uufz4th/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Виктор Цой (Viktor Tsoi)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/8mogk3d7) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/8mogk3d7/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/viktor-tsoi')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/viktor-tsoi")

model = AutoModelWithLMHead.from_pretrained("huggingartists/viktor-tsoi")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: viktor-tsoi


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/viktor-tsoi, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/twenty-one-pilots
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

twenty one pilots

@twenty-one-pilots


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from twenty one pilots.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/twenty-one-pilots).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/twenty-one-pilots")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2wr3j4nk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on twenty one pilots's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3jhgvd5t) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3jhgvd5t/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/twenty-one-pilots')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/twenty-one-pilots")

model = AutoModelWithLMHead.from_pretrained("huggingartists/twenty-one-pilots")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: twenty-one-pilots


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/twenty-one-pilots, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/placebo
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Placebo

@placebo


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Placebo.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/placebo).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/placebo")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3jfcdfc1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Placebo's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/jx3r5x9o) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/jx3r5x9o/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/placebo')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/placebo")

model = AutoModelWithLMHead.from_pretrained("huggingartists/placebo")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: placebo


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/placebo, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/i-dont-know-how-but-they-found-me
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

I DONT KNOW HOW BUT THEY FOUND ME

@i-dont-know-how-but-they-found-me


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from I DONT KNOW HOW BUT THEY FOUND ME.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/i-dont-know-how-but-they-found-me).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/i-dont-know-how-but-they-found-me")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1j7uofwh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on I DONT KNOW HOW BUT THEY FOUND ME's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1abhthz2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1abhthz2/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/i-dont-know-how-but-they-found-me')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/i-dont-know-how-but-they-found-me")

model = AutoModelWithLMHead.from_pretrained("huggingartists/i-dont-know-how-but-they-found-me")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: i-dont-know-how-but-they-found-me


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/i-dont-know-how-but-they-found-me, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/our-last-night
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Our Last Night

@our-last-night


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Our Last Night.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/our-last-night).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/our-last-night")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/37o66f2j/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Our Last Night's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1hifralf) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1hifralf/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/our-last-night')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/our-last-night")

model = AutoModelWithLMHead.from_pretrained("huggingartists/our-last-night")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: our-last-night


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/our-last-night, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/the-gazette
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

The Gazette

@the-gazette


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from The Gazette.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-gazette).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-gazette")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3ck1sdfv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on The Gazette's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/m1wevlws) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/m1wevlws/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/the-gazette')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-gazette")

model = AutoModelWithLMHead.from_pretrained("huggingartists/the-gazette")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: the-gazette


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/the-gazette, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/kizaru
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

kizaru

@kizaru


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from kizaru.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/kizaru).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/kizaru")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2goru0fu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on kizaru's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1zni18k7) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1zni18k7/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/kizaru')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/kizaru")

model = AutoModelWithLMHead.from_pretrained("huggingartists/kizaru")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: kizaru


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/kizaru, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/travis-scott
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Travis Scott

@travis-scott


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Travis Scott.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/travis-scott).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/travis-scott")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1ezlbvd0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Travis Scott's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2w91gglb) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2w91gglb/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/travis-scott')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/travis-scott")

model = AutoModelWithLMHead.from_pretrained("huggingartists/travis-scott")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: travis-scott


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/travis-scott, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/elton-john
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Elton John

@elton-john


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Elton John.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/elton-john).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/elton-john")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/4l9f8jif/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Elton John's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/9glyfrcq) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/9glyfrcq/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/elton-john')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/elton-john")

model = AutoModelWithLMHead.from_pretrained("huggingartists/elton-john")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: elton-john


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/elton-john, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/mikhail-gorshenev
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Михаил Горшенев (Mikhail Gorshenev)

@mikhail-gorshenev


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Михаил Горшенев (Mikhail Gorshenev).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/mikhail-gorshenev).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/mikhail-gorshenev")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3h9endcz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Михаил Горшенев (Mikhail Gorshenev)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1kdp29bz) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1kdp29bz/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/mikhail-gorshenev')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/mikhail-gorshenev")

model = AutoModelWithLMHead.from_pretrained("huggingartists/mikhail-gorshenev")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: mikhail-gorshenev


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/mikhail-gorshenev, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/as-i-lay-dying
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

As I Lay Dying

@as-i-lay-dying


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from As I Lay Dying.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/as-i-lay-dying).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/as-i-lay-dying")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2zq9ub8b/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on As I Lay Dying's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/cjg5ac7f) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/cjg5ac7f/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/as-i-lay-dying')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/as-i-lay-dying")

model = AutoModelWithLMHead.from_pretrained("huggingartists/as-i-lay-dying")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: as-i-lay-dying


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/as-i-lay-dying, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/burzum
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Burzum

@burzum


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Burzum.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/burzum).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/burzum")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/j34qgww2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Burzum's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3579mrib) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3579mrib/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/burzum')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/burzum")

model = AutoModelWithLMHead.from_pretrained("huggingartists/burzum")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: burzum


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/burzum, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/deep-purple
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Deep Purple

@deep-purple


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Deep Purple.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/deep-purple).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/deep-purple")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2sybcajo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Deep Purple's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3evu15qv) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3evu15qv/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/deep-purple')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/deep-purple")

model = AutoModelWithLMHead.from_pretrained("huggingartists/deep-purple")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: deep-purple


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/deep-purple, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/andre-3000
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

André 3000

@andre-3000


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from André 3000.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/andre-3000).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/andre-3000")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2hnhboqf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on André 3000's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1mydp6nh) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1mydp6nh/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/andre-3000')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/andre-3000")

model = AutoModelWithLMHead.from_pretrained("huggingartists/andre-3000")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: andre-3000


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/andre-3000, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/ghostemane
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Ghostemane

@ghostemane


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Ghostemane.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/ghostemane).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/ghostemane")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1ou29taa/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Ghostemane's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/futdflju) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/futdflju/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/ghostemane')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/ghostemane")

model = AutoModelWithLMHead.from_pretrained("huggingartists/ghostemane")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: ghostemane


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/ghostemane, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/the-grateful-dead
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

The Grateful Dead

@the-grateful-dead


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from The Grateful Dead.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-grateful-dead).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-grateful-dead")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2agvlyoo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on The Grateful Dead's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1ex4c8kc) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1ex4c8kc/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/the-grateful-dead')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-grateful-dead")

model = AutoModelWithLMHead.from_pretrained("huggingartists/the-grateful-dead")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: the-grateful-dead


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/the-grateful-dead, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/egor-letov
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Егор Летов (Egor Letov)

@egor-letov


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Егор Летов (Egor Letov).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/egor-letov).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/egor-letov")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/hya2wr54/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Егор Летов (Egor Letov)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2xmn5btx) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2xmn5btx/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/egor-letov')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/egor-letov")

model = AutoModelWithLMHead.from_pretrained("huggingartists/egor-letov")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: egor-letov


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/egor-letov, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/aaron-watson
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Aaron Watson

@aaron-watson


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Aaron Watson.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/aaron-watson).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/aaron-watson")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/14ha1tnc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Aaron Watson's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/34e4zb2v) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/34e4zb2v/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/aaron-watson')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/aaron-watson")

model = AutoModelWithLMHead.from_pretrained("huggingartists/aaron-watson")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: aaron-watson


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/aaron-watson, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/the-velvet-underground
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

The Velvet Underground

@the-velvet-underground


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from The Velvet Underground.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-velvet-underground).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-velvet-underground")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/lbkqy84q/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on The Velvet Underground's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1e4s74q4) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1e4s74q4/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/the-velvet-underground')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-velvet-underground")

model = AutoModelWithLMHead.from_pretrained("huggingartists/the-velvet-underground")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: the-velvet-underground


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/the-velvet-underground, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/slava-marlow
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

SLAVA MARLOW

@slava-marlow


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from SLAVA MARLOW.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/slava-marlow).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/slava-marlow")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1fdcz1s5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on SLAVA MARLOW's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/ro4q353s) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/ro4q353s/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/slava-marlow')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/slava-marlow")

model = AutoModelWithLMHead.from_pretrained("huggingartists/slava-marlow")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: slava-marlow


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/slava-marlow, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/pyrokinesis
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

pyrokinesis

@pyrokinesis


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from pyrokinesis.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/pyrokinesis).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/pyrokinesis")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1s8696f3/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on pyrokinesis's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/22hm2utc) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/22hm2utc/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/pyrokinesis')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/pyrokinesis")

model = AutoModelWithLMHead.from_pretrained("huggingartists/pyrokinesis")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: pyrokinesis


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/pyrokinesis, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/red-hot-chili-peppers
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Red Hot Chili Peppers

@red-hot-chili-peppers


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Red Hot Chili Peppers.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/red-hot-chili-peppers).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/red-hot-chili-peppers")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2spp06qm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Red Hot Chili Peppers's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/opiwx19q) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/opiwx19q/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/red-hot-chili-peppers')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/red-hot-chili-peppers")

model = AutoModelWithLMHead.from_pretrained("huggingartists/red-hot-chili-peppers")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: red-hot-chili-peppers


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/red-hot-chili-peppers, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/enya
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Enya

@enya


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Enya.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/enya).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/enya")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/16cuy8yb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Enya's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/il8ldqo8) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/il8ldqo8/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/enya')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/enya")

model = AutoModelWithLMHead.from_pretrained("huggingartists/enya")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: enya


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/enya, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/tanzy-minus
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Танцы Минус (Tanzy Minus)

@tanzy-minus


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Танцы Минус (Tanzy Minus).


Dataset is available [here](https://huggingface.co/datasets/huggingartists/tanzy-minus).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/tanzy-minus")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/14vmwaxq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Танцы Минус (Tanzy Minus)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/ru5wxieh) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/ru5wxieh/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/tanzy-minus')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/tanzy-minus")

model = AutoModelWithLMHead.from_pretrained("huggingartists/tanzy-minus")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: tanzy-minus


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/tanzy-minus, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/coldplay
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Coldplay

@coldplay


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Coldplay.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/coldplay).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/coldplay")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/399heq03/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Coldplay's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3o6fr9bq) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3o6fr9bq/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/coldplay')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/coldplay")

model = AutoModelWithLMHead.from_pretrained("huggingartists/coldplay")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: coldplay


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/coldplay, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/kehlani
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Kehlani

@kehlani


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Kehlani.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/kehlani).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/kehlani")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3t2b2m5y/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Kehlani's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/35pweb11) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/35pweb11/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/kehlani')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/kehlani")

model = AutoModelWithLMHead.from_pretrained("huggingartists/kehlani")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: kehlani


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/kehlani, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/lazy-jay
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Lazy Jay

@lazy-jay


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Lazy Jay.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/lazy-jay).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/lazy-jay")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/tlb735a4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Lazy Jay's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/36z52xfj) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/36z52xfj/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/lazy-jay')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/lazy-jay")

model = AutoModelWithLMHead.from_pretrained("huggingartists/lazy-jay")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: lazy-jay


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/lazy-jay, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/bring-me-the-horizon
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Bring Me The Horizon

@bring-me-the-horizon


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from Bring Me The Horizon.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/bring-me-the-horizon).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/bring-me-the-horizon")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1e9181i6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Bring Me The Horizon's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3p7pncir) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3p7pncir/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/bring-me-the-horizon')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/bring-me-the-horizon")

model = AutoModelWithLMHead.from_pretrained("huggingartists/bring-me-the-horizon")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)


model name: bring-me-the-horizon


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/bring-me-the-horizon, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/the-notorious-big
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

The Notorious B.I.G.

@the-notorious-big


I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).


Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!


## How does it work?


To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).


## Training data


The model was trained on lyrics from The Notorious B.I.G..


Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-notorious-big).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-notorious-big")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/wkvasju4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on The Notorious B.I.G.'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1coezuy2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1coezuy2/artifacts) is logged and versioned.


## How to use


You can use this model directly with a pipeline for text generation:


```python
from transformers import pipeline
generator = pipeline('text-generation',
                     model='huggingartists/the-notorious-big')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-notorious-big")

model = AutoModelWithLMHead.from_pretrained("huggingartists/the-notorious-big")
```


## Limitations and bias


The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).


In addition, the data present in the user's tweets further affects the text generated by the model.


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)