Llama-JP-0.5B-init

English / Japanese

Overview

Llama-JP-0.5B-init is a Japanese initialization of the Llama architecture with approximately 0.5B non-embedding parameters. The checkpoint serves as a clean starting point for downstream pre-training or instruction tuning rather than a production-ready model.

• 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

• Architecture: Llama
• Maximum sequence length: 8,192 tokens
• Embedding dimension: 1280
• Tokenizer: SentencePiece / vocabulary size 102,400
• Positional encoding: RoPE
• Supported languages: Japanese

Model Series

This table lists the initialization checkpoints prior to any domain-specific training. All variants share the sarashina2.2 tokenizer.

Licence

This model is distributed under the MIT License.

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}
}