Instructions to use bankholdup/rugpt3_song_writer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bankholdup/rugpt3_song_writer with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bankholdup/rugpt3_song_writer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bankholdup/rugpt3_song_writer")
model = AutoModelForCausalLM.from_pretrained("bankholdup/rugpt3_song_writer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bankholdup/rugpt3_song_writer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bankholdup/rugpt3_song_writer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bankholdup/rugpt3_song_writer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bankholdup/rugpt3_song_writer

SGLang

How to use bankholdup/rugpt3_song_writer with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bankholdup/rugpt3_song_writer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bankholdup/rugpt3_song_writer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bankholdup/rugpt3_song_writer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bankholdup/rugpt3_song_writer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bankholdup/rugpt3_song_writer with Docker Model Runner:
```
docker model run hf.co/bankholdup/rugpt3_song_writer
```

rugpt3_song_writer / README.md

bankholdup

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909ce17 over 4 years ago

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metadata

language:
  - ru
tags:
  - PyTorch
  - Transformers
widget:
  - text: Батя возвращается трезвый, в руке буханка
    example_title: Example 1
  - text: Как дела? Как дела? Это новый кадиллак
    example_title: Example 2
  - text: 4:20 на часах и я дрочу на твоё фото
    example_title: Example 3
inference:
  parameters:
    temperature: 0.9
    k: 50
    p: 0.95
    length: 1500

Model based on ruGPT-3 for generating songs. Tuned on lyrics collected from genius. Examples of used artists: