Instructions to use stockmark/gpt-neox-japanese-1.4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stockmark/gpt-neox-japanese-1.4b with Transformers:

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

pipe = pipeline("text-generation", model="stockmark/gpt-neox-japanese-1.4b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stockmark/gpt-neox-japanese-1.4b")
model = AutoModelForCausalLM.from_pretrained("stockmark/gpt-neox-japanese-1.4b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use stockmark/gpt-neox-japanese-1.4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stockmark/gpt-neox-japanese-1.4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stockmark/gpt-neox-japanese-1.4b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/stockmark/gpt-neox-japanese-1.4b

SGLang

How to use stockmark/gpt-neox-japanese-1.4b 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 "stockmark/gpt-neox-japanese-1.4b" \
    --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": "stockmark/gpt-neox-japanese-1.4b",
		"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 "stockmark/gpt-neox-japanese-1.4b" \
        --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": "stockmark/gpt-neox-japanese-1.4b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use stockmark/gpt-neox-japanese-1.4b with Docker Model Runner:
```
docker model run hf.co/stockmark/gpt-neox-japanese-1.4b
```

stockmark/gpt-neox-japanese-1.4b

This repository provides a GPT-NeoX based model with 1.4B parameters pre-trained on Japanese corpus of about 20B tokens. This model is developed by Stockmark Inc.

How to use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Use torch.bfloat16 for A100 GPU and torch.flaot16 for the older generation GPUs
torch_dtype = torch.bfloat16 if torch.cuda.is_available() and hasattr(torch.cuda, "is_bf16_supported") and torch.cuda.is_bf16_supported() else torch.float16

model = AutoModelForCausalLM.from_pretrained("stockmark/gpt-neox-japanese-1.4b", device_map="auto", torch_dtype=torch_dtype)
tokenizer = AutoTokenizer.from_pretrained("stockmark/gpt-neox-japanese-1.4b")

inputs = tokenizer("自然言語処理は", return_tensors="pt").to(model.device)
with torch.no_grad():
    tokens = model.generate(
        **inputs,
        max_new_tokens=128,
        repetition_penalty=1.1
    )
    
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)