Instructions to use skytnt/gpt2-japanese-lyric-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skytnt/gpt2-japanese-lyric-medium with Transformers:

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

pipe = pipeline("text-generation", model="skytnt/gpt2-japanese-lyric-medium")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("skytnt/gpt2-japanese-lyric-medium")
model = AutoModelForCausalLM.from_pretrained("skytnt/gpt2-japanese-lyric-medium", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use skytnt/gpt2-japanese-lyric-medium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "skytnt/gpt2-japanese-lyric-medium"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "skytnt/gpt2-japanese-lyric-medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/skytnt/gpt2-japanese-lyric-medium

SGLang

How to use skytnt/gpt2-japanese-lyric-medium 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 "skytnt/gpt2-japanese-lyric-medium" \
    --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": "skytnt/gpt2-japanese-lyric-medium",
		"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 "skytnt/gpt2-japanese-lyric-medium" \
        --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": "skytnt/gpt2-japanese-lyric-medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use skytnt/gpt2-japanese-lyric-medium with Docker Model Runner:
```
docker model run hf.co/skytnt/gpt2-japanese-lyric-medium
```

gpt2-japanese-lyric-medium / tf_model.h5

skytnt

init

ca27167 about 4 years ago

Download

History Blame Contribute Delete

1.34 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 92b9f0f2e129ec80300122f538fd46b987acfe1914f09ad5a09c4907ddfae907
Size of remote file:: 1.34 GB
SHA256:: 9330c28c162925e8cf10acdb88f307da4dfc952695be5e571ae8d36b33f4068e

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.