model name: the-notorious-big


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/lovv66
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

LOVV66

@lovv66


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 LOVV66.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/lovv66).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/lovv66")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1t6a2fxs/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 LOVV66's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1de08pf6) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1de08pf6/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/lovv66')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/lovv66")

model = AutoModelWithLMHead.from_pretrained("huggingartists/lovv66")
```


## 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: lovv66


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/lovv66, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

HyunA (현아)

@hyuna


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 HyunA (현아).


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3uo94mxd/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 HyunA (현아)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1o8t0mq0) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1o8t0mq0/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/hyuna')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: hyuna


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Tool

@tool


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 Tool.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3ql2lz0c/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 Tool's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/vc1gg3ig) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/vc1gg3ig/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/tool')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: tool


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Джизус (Dzhizus)

@dzhizus


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 Джизус (Dzhizus).


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/35paacn1/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 Джизус (Dzhizus)'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1ug3yebo) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1ug3yebo/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/dzhizus')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: dzhizus


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Ed Sheeran

@ed-sheeran


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 Ed Sheeran.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/ed-sheeran")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3nju68bo/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 Ed Sheeran's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3hu7zc76) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3hu7zc76/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/ed-sheeran')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/ed-sheeran")

model = AutoModelWithLMHead.from_pretrained("huggingartists/ed-sheeran")
```


## 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: ed-sheeran


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Mating Ritual

@mating-ritual


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 Mating Ritual.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/mating-ritual")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3cljintu/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 Mating Ritual's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/dv1g3x3b) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/dv1g3x3b/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/mating-ritual')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/mating-ritual")

model = AutoModelWithLMHead.from_pretrained("huggingartists/mating-ritual")
```


## 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: mating-ritual


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Led Zeppelin

@led-zeppelin


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 Led Zeppelin.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/led-zeppelin")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/170kpeuy/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 Led Zeppelin's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/39r70n5b) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/39r70n5b/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/led-zeppelin')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/led-zeppelin")

model = AutoModelWithLMHead.from_pretrained("huggingartists/led-zeppelin")
```


## 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: led-zeppelin


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Bryan Adams

@bryan-adams


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 Bryan Adams.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/bryan-adams")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/22ksbpsz/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 Bryan Adams's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3b0c22fu) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3b0c22fu/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/bryan-adams')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/bryan-adams")

model = AutoModelWithLMHead.from_pretrained("huggingartists/bryan-adams")
```


## 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: bryan-adams


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

ROCKET

@rocket


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 ROCKET.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3ceqmb05/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 ROCKET's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/37kckftd) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/37kckftd/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/rocket')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: rocket


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Logic

@logic


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 Logic.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2rp89nd3/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 Logic's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/25a9752b) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/25a9752b/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/logic')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: logic


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Alan Walker

@alan-walker


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 Alan Walker.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/alan-walker")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3oyxxcos/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 Alan Walker's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/huoxll6m) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/huoxll6m/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/alan-walker')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/alan-walker")

model = AutoModelWithLMHead.from_pretrained("huggingartists/alan-walker")
```


## 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: alan-walker


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Queen

@queen


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 Queen.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1v5o4ijc/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 Queen's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2apdiv6y) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2apdiv6y/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/queen')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: queen


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Kurt Cobain

@kurt-cobain


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 Kurt Cobain.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/kurt-cobain")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/tjfuj6tr/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 Kurt Cobain's lyrics.


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/kurt-cobain")

model = AutoModelWithLMHead.from_pretrained("huggingartists/kurt-cobain")
```


## 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: kurt-cobain


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Eminem

@eminem


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 Eminem.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/wqeebjm6/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 Eminem's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1r91z1u3) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1r91z1u3/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/eminem')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: eminem


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/the-69-eyes
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

The 69 Eyes

@the-69-eyes


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


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


## How does it work?


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


## Training data


The model was trained on lyrics from The 69 Eyes.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-69-eyes).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/the-69-eyes")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/26sibipb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.


## Training procedure


The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on The 69 Eyes's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1mjcdm16) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1mjcdm16/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-69-eyes')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-69-eyes")

model = AutoModelWithLMHead.from_pretrained("huggingartists/the-69-eyes")
```


## 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-69-eyes


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/the-69-eyes, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Gunna

@gunna


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 Gunna.


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


```python
from datasets import load_dataset

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


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3c1xymw6) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3c1xymw6/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/gunna')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: gunna


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Madonna

@madonna


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 Madonna.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1zgkgxrp/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 Madonna's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1nejy753) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1nejy753/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/madonna')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: madonna


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Tiamat

@tiamat


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 Tiamat.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1tqzwb4a/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 Tiamat's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/ttkys3mq) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/ttkys3mq/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/tiamat')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: tiamat


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/system-of-a-down
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

System of a Down

@system-of-a-down


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 System of a Down.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/system-of-a-down).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/system-of-a-down")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3m1sikv8/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 System of a Down's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/wf3qe4yi) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/wf3qe4yi/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/system-of-a-down')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/system-of-a-down")

model = AutoModelWithLMHead.from_pretrained("huggingartists/system-of-a-down")
```


## 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: system-of-a-down


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/system-of-a-down, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

DaBaby

@dababy


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 DaBaby.


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


```python
from datasets import load_dataset

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


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


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/24o367up) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/24o367up/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/dababy')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: dababy


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Ramil’

@ramil


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 Ramil’.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1l1axl7k/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 Ramil’'s lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/28boyxm8) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/28boyxm8/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/ramil')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: ramil


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/armin-van-buuren
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Armin van Buuren

@armin-van-buuren


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 Armin van Buuren.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/armin-van-buuren")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/hrrfc55y/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 Armin van Buuren's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3q93rwo8) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3q93rwo8/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/armin-van-buuren')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/armin-van-buuren")

model = AutoModelWithLMHead.from_pretrained("huggingartists/armin-van-buuren")
```


## 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: armin-van-buuren


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Dua Lipa

@dua-lipa


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 Dua Lipa.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/dua-lipa")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2wxz1liw/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 Dua Lipa's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3uj930yj) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3uj930yj/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/dua-lipa')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/dua-lipa")

model = AutoModelWithLMHead.from_pretrained("huggingartists/dua-lipa")
```


## 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: dua-lipa


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Krechet

@krechet


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 Krechet.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1c2yk38s/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 Krechet's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/39bxkroc) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/39bxkroc/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/krechet')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: krechet


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Lil Baby

@lil-baby


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 Lil Baby.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/lil-baby")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/vueaothh/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 Lil Baby's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/257bod1h) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/257bod1h/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/lil-baby')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/lil-baby")

model = AutoModelWithLMHead.from_pretrained("huggingartists/lil-baby")
```


## 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: lil-baby


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Marillion

@marillion


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 Marillion.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/bajnt52i/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 Marillion's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/wi2lgudb) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/wi2lgudb/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/marillion')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: marillion


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Little Big

@little-big


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


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


## How does it work?


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


## Training data


The model was trained on lyrics from Little Big.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/little-big")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2rjstm9q/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 Little Big's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/289c46fn) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/289c46fn/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/little-big')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## Limitations and bias


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


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


## About


*Built by Aleksey Korshuk*


For more details, visit the project repository.


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


model name: little-big


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/big-russian-boss
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Big Russian Boss

@big-russian-boss


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 Big Russian Boss.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/big-russian-boss")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1ju9bqqi/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 Big Russian Boss's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3820n7qx) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3820n7qx/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/big-russian-boss')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/big-russian-boss")

model = AutoModelWithLMHead.from_pretrained("huggingartists/big-russian-boss")
```


## 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: big-russian-boss


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/6ix9ine
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

6ix9ine

@6ix9ine


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 6ix9ine.


Dataset is available [here](https://huggingface.co/datasets/huggingartists/6ix9ine).
And can be used with:


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/6ix9ine")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/eqmcaj0r/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 6ix9ine's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/s5dpg3h2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/s5dpg3h2/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/6ix9ine')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/6ix9ine")

model = AutoModelWithLMHead.from_pretrained("huggingartists/6ix9ine")
```


## 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: 6ix9ine


model author: huggingartists


task: text-generation


tags: pytorch, jax, gpt2, text-generation, en, dataset:huggingartists/6ix9ine, transformers, huggingartists, lyrics, lm-head, causal-lm


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Lil Peep

@lil-peep


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 Lil Peep.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/lil-peep")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/39q6kspr/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 Lil Peep's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/g0nxk974) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/g0nxk974/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/lil-peep')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/lil-peep")

model = AutoModelWithLMHead.from_pretrained("huggingartists/lil-peep")
```


## 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: lil-peep


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/lil-uzi-vert
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Lil Uzi Vert

@lil-uzi-vert


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 Lil Uzi Vert.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/lil-uzi-vert")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2ndgi0dn/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 Lil Uzi Vert's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/9x6wbf6e) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/9x6wbf6e/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/lil-uzi-vert')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/lil-uzi-vert")

model = AutoModelWithLMHead.from_pretrained("huggingartists/lil-uzi-vert")
```


## 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: lil-uzi-vert


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

MAYOT

@mayot


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 MAYOT.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/lf4wcx85/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 MAYOT's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1uulibm2) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1uulibm2/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/mayot')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: mayot


model author: huggingartists


task: text-generation


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


language: en
datasets:


- huggingartists/rex-orange-county
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"


🤖 HuggingArtists Model 🤖

Rex Orange County

@rex-orange-county


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 Rex Orange County.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/rex-orange-county")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3by3xc64/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 Rex Orange County's lyrics.


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


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


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/rex-orange-county")

model = AutoModelWithLMHead.from_pretrained("huggingartists/rex-orange-county")
```


## 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: rex-orange-county


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Bruce Springsteen

@bruce-springsteen


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 Bruce Springsteen.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/bruce-springsteen")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/28yd4w57/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 Bruce Springsteen's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/6qq7wbab) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/6qq7wbab/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/bruce-springsteen')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/bruce-springsteen")

model = AutoModelWithLMHead.from_pretrained("huggingartists/bruce-springsteen")
```


## 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: bruce-springsteen


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Mnogoznaal

@mnogoznaal


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 Mnogoznaal.


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


```python
from datasets import load_dataset

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


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/21uo4oav/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 Mnogoznaal's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/13v4iqfe) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/13v4iqfe/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/mnogoznaal')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

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

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


## 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: mnogoznaal


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Fear Factory

@fear-factory


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 Fear Factory.


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


```python
from datasets import load_dataset

dataset = load_dataset("huggingartists/fear-factory")
```


[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/24xjxpf5/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 Fear Factory's lyrics.


Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3gju7udi) for full transparency and reproducibility.


At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3gju7udi/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/fear-factory')
generator("I am", num_return_sequences=5)
```


Or with Transformers library:


```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("huggingartists/fear-factory")

model = AutoModelWithLMHead.from_pretrained("huggingartists/fear-factory")
```


## 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: fear-factory


model author: huggingartists


task: text-generation


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


language: en
datasets:


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


🤖 HuggingArtists Model 🤖

Enigma

@enigma