Instructions to use iamtatsuki05/Llama-JP-0.5B-init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iamtatsuki05/Llama-JP-0.5B-init with Transformers:

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

pipe = pipeline("text-generation", model="iamtatsuki05/Llama-JP-0.5B-init")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iamtatsuki05/Llama-JP-0.5B-init")
model = AutoModelForCausalLM.from_pretrained("iamtatsuki05/Llama-JP-0.5B-init", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use iamtatsuki05/Llama-JP-0.5B-init with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iamtatsuki05/Llama-JP-0.5B-init"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamtatsuki05/Llama-JP-0.5B-init",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/iamtatsuki05/Llama-JP-0.5B-init

SGLang

How to use iamtatsuki05/Llama-JP-0.5B-init 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 "iamtatsuki05/Llama-JP-0.5B-init" \
    --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": "iamtatsuki05/Llama-JP-0.5B-init",
		"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 "iamtatsuki05/Llama-JP-0.5B-init" \
        --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": "iamtatsuki05/Llama-JP-0.5B-init",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use iamtatsuki05/Llama-JP-0.5B-init with Docker Model Runner:
```
docker model run hf.co/iamtatsuki05/Llama-JP-0.5B-init
```

Llama-JP-0.5B-init / README_JA.md

iamtatsuki05

Update README_JA.md

e7b2a4f verified 9 months ago

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metadata

language:
  - ja
license: mit
library_name: transformers
pipeline_tag: text-generation
tags:
  - mirei
  - llama

Llama-JP-0.5B-init

English / Japanese

Overview

Llama-JP-0.5B-init は、非埋め込みパラメータが約 0.5B パラメータの日本語の Llama モデルです。初期化済みの重みであるため、この段階のモデル単体での実務利用は想定していません。

Hugging Face Collection
GitHub

Usage

Requirements

transformers>=4.51.0
accelerate>=1.6.0
sentencepiece>=0.2.0
flash-attn>=2.7.3

Sample Code

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "iamtatsuki05/Llama-JP-0.5B-init"
model_kwargs = {
  "torch_dtype": torch.bfloat16,
  "attn_implementation": "flash_attention_2",
  "device_map": "auto",
}
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, **model_kwargs)

prompt = "ちいかわのハチワレは"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.8,
    top_p=0.9,
    do_sample=True,
)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Model Details

アーキテクチャ: Llama
最大シーケンス長: 8,192トークン
埋め込み次元: 1280
トークナイザ: SentencePiece / 語彙数 102,400
位置エンコーディング: RoPE
対応言語: 日本語

Model Series

学習前に初期化したモデル群です。 tokenizer には sarashina2.2 tokenizer を使用しています。

ID	Architecture	#Param.	#Param. w/o Emb.
iamtatsuki05/ModernBERT-JP-0.5B-init	ModernBERT	679M	548M
iamtatsuki05/Llama-JP-0.5B-init (this model)	Llama	661M	530M

Licence

このモデルは MIT でライセンスされています。

How to Cite

@article{MIREI
  title={同一条件下における Encoder/Decoder アーキテクチャによる文埋め込みの性能分析},
  author={岡田 龍樹 and 杉本 徹},
  journal={言語処理学会第 32 回年次大会 (NLP2026)},
  year={2026}
}