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

Libraries

How to use rinna/japanese-gpt2-medium with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-medium")
model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-medium")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rinna/japanese-gpt2-medium with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rinna/japanese-gpt2-medium

SGLang

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

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

japanese-gpt2-medium

This repository provides a medium-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.

How to use the model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-medium", use_fast=False)
tokenizer.do_lower_case = True  # due to some bug of tokenizer config loading

model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-medium")

Model architecture

A 24-layer, 1024-hidden-size transformer-based language model.

Training

The model was trained on Japanese CC-100 and Japanese Wikipedia to optimize a traditional language modelling objective on 8\*V100 GPUs for around 30 days. It reaches around 18 perplexity on a chosen validation set from the same data.

Tokenization

The model uses a sentencepiece-based tokenizer, the vocabulary was trained on the Japanese Wikipedia using the official sentencepiece training script.

Release date

April 7, 2021 (Updated: August 25, 2021)

How to cite

@misc{rinna-japanese-gpt2-medium,
    title = {rinna/japanese-gpt2-medium},
    author = {Zhao, Tianyu and Sawada, Kei},
    url = {https://huggingface.co/rinna/japanese-gpt2-medium}
}

@inproceedings{sawada2024release,
    title = {Release of Pre-Trained Models for the {J}apanese Language},
    author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
    booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
    month = {5},
    year = {2024},
    pages = {13898--13905},
    url = {https://aclanthology.org/2024.lrec-main.1213},
    note = {\url{https://arxiv.org/abs/2404.01657}}
}