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


model name: dj-artem-artemov


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Kanye West

@kanye-west


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 Kanye West.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/kanye-west")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1nriijf4/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 Kanye West's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2qxzz0h9) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2qxzz0h9/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/kanye-west')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/kanye-west")

model = AutoModelWithLMHead.from_pretrained("huggingartists/kanye-west")
```


## 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: kanye-west


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Linkin Park

@linkin-park


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 Linkin Park.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/linkin-park")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3mtr0u4z/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 Linkin Park's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/fxn4brd6) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/fxn4brd6/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/linkin-park')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/linkin-park")

model = AutoModelWithLMHead.from_pretrained("huggingartists/linkin-park")
```


## 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: linkin-park


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Billy Talent

@billy-talent


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 Billy Talent.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/billy-talent")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/37amfbe8/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 Billy Talent's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/pyw6tj9v) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/pyw6tj9v/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/billy-talent')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/billy-talent")

model = AutoModelWithLMHead.from_pretrained("huggingartists/billy-talent")
```


## 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: billy-talent


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/25-17
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

25/17

@25-17


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 25/17.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/25-17).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/25-17")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1iuytbjp/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 25/17's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/knv4l4gw) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/knv4l4gw/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/25-17')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/25-17")

model = AutoModelWithLMHead.from_pretrained("huggingartists/25-17")
```


## 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: 25-17


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/25-17, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Bob Dylan

@bob-dylan


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 Bob Dylan.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/bob-dylan")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/12pi332s/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 Bob Dylan's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/34f7xa4s) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/34f7xa4s/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/bob-dylan')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/bob-dylan")

model = AutoModelWithLMHead.from_pretrained("huggingartists/bob-dylan")
```


## 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: bob-dylan


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Imagine Dragons

@imagine-dragons


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 Imagine Dragons.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/imagine-dragons")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/dln6ixis/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 Imagine Dragons's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3cj3c8z1) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3cj3c8z1/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/imagine-dragons')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/imagine-dragons")

model = AutoModelWithLMHead.from_pretrained("huggingartists/imagine-dragons")
```


## 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: imagine-dragons


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

shadowraze

@shadowraze


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 shadowraze.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/pkbkflsq/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 shadowraze's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/tiu2mjo1) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/tiu2mjo1/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/shadowraze')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: shadowraze


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Bladee

@bladee


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 Bladee.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/326nmhkf/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 Bladee's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/28bmutxl) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/28bmutxl/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/bladee')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: bladee


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Машина Времени (Mashina Vremeni)

@mashina-vremeni


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 Машина Времени (Mashina Vremeni).


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/mashina-vremeni")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3r1yxrx7/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 Машина Времени (Mashina Vremeni)'s lyrics.


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


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1cgaltpc/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/mashina-vremeni')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/mashina-vremeni")

model = AutoModelWithLMHead.from_pretrained("huggingartists/mashina-vremeni")
```


## 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: mashina-vremeni


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Sqwore

@sqwore


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 Sqwore.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3gzd5crq/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 Sqwore's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/vzeft23g) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/vzeft23g/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/sqwore')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: sqwore


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Muse

@muse


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 Muse.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3w58rwod/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 Muse's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3j03atcr) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3j03atcr/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/muse')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: muse


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Макс Корж (Max Korzh)

@max-korzh


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 Макс Корж (Max Korzh).


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/max-korzh")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2lupo5gy/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 Макс Корж (Max Korzh)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1pm64gaa) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1pm64gaa/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/max-korzh')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/max-korzh")

model = AutoModelWithLMHead.from_pretrained("huggingartists/max-korzh")
```


## 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: max-korzh


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

LoveRance

@loverance


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 LoveRance.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2cr3cjd1/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 LoveRance's lyrics.


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


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/18xbgyqf/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/loverance')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: loverance


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/50-cent
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

50 Cent

@50-cent


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 50 Cent.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/50-cent).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/50-cent")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2iekmvbo/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 50 Cent's lyrics.


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


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1zrybuom/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/50-cent')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/50-cent")

model = AutoModelWithLMHead.from_pretrained("huggingartists/50-cent")
```


## 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: 50-cent


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/50-cent, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Bill Wurtz

@bill-wurtz


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 Bill Wurtz.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/bill-wurtz")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1y01b0sy/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 Bill Wurtz's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1l9hr5w2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1l9hr5w2/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/bill-wurtz')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/bill-wurtz")

model = AutoModelWithLMHead.from_pretrained("huggingartists/bill-wurtz")
```


## 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: bill-wurtz


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Green Day

@green-day


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 Green Day.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/green-day")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/22eap04b/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 Green Day's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/183da0m9) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/183da0m9/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/green-day')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/green-day")

model = AutoModelWithLMHead.from_pretrained("huggingartists/green-day")
```


## 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: green-day


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

O.T (RUS)

@ot-rus


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 O.T (RUS).


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/ot-rus")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/35byet4r/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 O.T (RUS)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2p2tawej) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2p2tawej/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/ot-rus')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/ot-rus")

model = AutoModelWithLMHead.from_pretrained("huggingartists/ot-rus")
```


## 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: ot-rus


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/21-savage
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

21 Savage

@21-savage


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 21 Savage.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/21-savage).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/21-savage")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3lbkznnf/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 21 Savage's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1fw9b6m4) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1fw9b6m4/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/21-savage')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/21-savage")

model = AutoModelWithLMHead.from_pretrained("huggingartists/21-savage")
```


## 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: 21-savage


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/21-savage, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Post Malone

@post-malone


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 Post Malone.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/post-malone")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/5ig21wpy/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 Post Malone's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2ih9ntzv) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2ih9ntzv/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/post-malone')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/post-malone")

model = AutoModelWithLMHead.from_pretrained("huggingartists/post-malone")
```


## 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: post-malone


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Lumen

@lumen


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 Lumen.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2fkqbnvl/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 Lumen's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1vhfm4ch) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1vhfm4ch/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/lumen')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: lumen


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/machine-gun-kelly
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Machine Gun Kelly

@machine-gun-kelly


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 Machine Gun Kelly.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/machine-gun-kelly")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/33f2ce6m/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 Machine Gun Kelly's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2bbn6fvb) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2bbn6fvb/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/machine-gun-kelly')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/machine-gun-kelly")

model = AutoModelWithLMHead.from_pretrained("huggingartists/machine-gun-kelly")
```


## 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: machine-gun-kelly


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Платина (Platina)

@platina


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 Платина (Platina).


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2ih365j7/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 Платина (Platina)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1quasiz0) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1quasiz0/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/platina')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: platina


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/peter-paul-and-mary
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Peter, Paul and Mary

@peter-paul-and-mary


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 Peter, Paul and Mary.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/peter-paul-and-mary).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/peter-paul-and-mary")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/svwa6bev/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 Peter, Paul and Mary's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1s4mkr9x) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1s4mkr9x/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/peter-paul-and-mary')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/peter-paul-and-mary")

model = AutoModelWithLMHead.from_pretrained("huggingartists/peter-paul-and-mary")
```


## 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: peter-paul-and-mary


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/peter-paul-and-mary, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


- huggingartists/maroon-5
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Maroon 5

@maroon-5


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 Maroon 5.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/maroon-5).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/maroon-5")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/38629b22/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 Maroon 5's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2ylk8pym) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2ylk8pym/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/maroon-5')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/maroon-5")

model = AutoModelWithLMHead.from_pretrained("huggingartists/maroon-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: maroon-5


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/maroon-5, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

MF DOOM

@mf-doom


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 MF DOOM.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/mf-doom")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3lhrsfds/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 MF DOOM's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/vw48qbeh) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/vw48qbeh/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/mf-doom')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/mf-doom")

model = AutoModelWithLMHead.from_pretrained("huggingartists/mf-doom")
```


## 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: mf-doom


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Rihanna

@rihanna


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 Rihanna.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3g4a2qa9/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 Rihanna's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3du2fhxz) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3du2fhxz/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/rihanna')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: rihanna


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

ЕГОР КРИД (EGOR KREED)

@egor-kreed


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 КРИД (EGOR KREED).


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3l7nf6hj/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 КРИД (EGOR KREED)'s lyrics.


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


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1mtfkshl/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-kreed')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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-kreed


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Nirvana

@nirvana


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 Nirvana.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/plmwg217/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 Nirvana's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2aaxwzd4) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2aaxwzd4/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/nirvana')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: nirvana


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

John K. Samson

@john-k-samson


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 John K. Samson.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/john-k-samson")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2s15m338/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 John K. Samson's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/18ill893) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/18ill893/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/john-k-samson')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: john-k-samson


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Grimes

@grimes


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 Grimes.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3796ng30/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 Grimes's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/ourv0tjj) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/ourv0tjj/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/grimes')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: grimes


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Sam Kim (샘김)

@sam-kim


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 Sam Kim (샘김).


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/sam-kim")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/38e0f1wf/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 Sam Kim (샘김)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2rke2zbk) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2rke2zbk/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/sam-kim')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/sam-kim")

model = AutoModelWithLMHead.from_pretrained("huggingartists/sam-kim")
```


## 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: sam-kim


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/100-gecs
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

100 gecs

@100-gecs


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 100 gecs.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/100-gecs).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/100-gecs")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3c9j4tvq/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 100 gecs's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1v0ffa4e) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1v0ffa4e/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/100-gecs')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/100-gecs")

model = AutoModelWithLMHead.from_pretrained("huggingartists/100-gecs")
```


## 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: 100-gecs


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/100-gecs, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Fascinoma

@fascinoma


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 Fascinoma.


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


```python
from datasets import load_dataset

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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/za989b3u/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 Fascinoma's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/kklye04t) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/kklye04t/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/fascinoma')
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: fascinoma


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Suicideoscope

@suicideoscope


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 Suicideoscope.


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


```python
from datasets import load_dataset

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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/17opu10a/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 Suicideoscope's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2w46luqb) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2w46luqb/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/suicideoscope')
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: suicideoscope


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/the-king-and-the-jester
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Король и Шут (The King and the Jester)

@the-king-and-the-jester


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 Король и Шут (The King and the Jester).


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-king-and-the-jester")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1qw2ic95/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 Король и Шут (The King and the Jester)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/hhhj9047) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/hhhj9047/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-king-and-the-jester')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-king-and-the-jester")

model = AutoModelWithLMHead.from_pretrained("huggingartists/the-king-and-the-jester")
```


## 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-king-and-the-jester


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Cardi B

@cardi-b


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 Cardi B.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/cardi-b")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2794795e/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 Cardi B's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1buiv5nf) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1buiv5nf/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/cardi-b')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/cardi-b")

model = AutoModelWithLMHead.from_pretrained("huggingartists/cardi-b")
```


## 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.