Text Generation
Transformers
PyTorch
TensorFlow
JAX
Safetensors
Japanese
gpt2
lm
nlp
text-generation-inference
Instructions to use rinna/japanese-gpt2-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
What is the id 9 in japanese-gpt2-medium tokenizer?
#1
by gojiteji - opened
I'm trying to use japanese-gpt2-medium for my research.
I found that sometimes the tokenizer outputs id 9 on the head like below.
print(tokenizer("hello world").input_ids)
> [9, 22848, 463, 7375, 2]
print(tokenizer("dog").input_ids)
> [6832, 275, 2]
But it looks like number 9 decodes nothing.
print(tokenizer.decode([9]) ,len(tokenizer.decode([9])))
> 0
What does id 9 token mean? When fine-tuning, should id 9 be left?
It is a special symbol (meta symbol "▁" (U+2581)) produced by sentencepiece.
Please refer to the sentencepiece repo for details: https://github.com/google/sentencepiece
>>> tokenizer.tokenize("hello world")
['▁', 'hell', 'o', '▁world']
>>> tokenizer.tokenize("dog")
['▁do', 'g']
You can leave it as it is for finetuning.
Thank you for your reply.
I see.
I’ll do so.
gojiteji changed discussion status to closed