Overview
Llama-JP-0.5B-PT-stage1 continues from iamtatsuki05/Llama-JP-0.5B-init and is trained on hotchpotch/fineweb-2-edu-japanese. The model observes approximately 10B tokens with 1,024-token context windows, providing a decoder-only Japanese backbone for downstream generative tasks.

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-PT-stage1"
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
- Base model: iamtatsuki05/Llama-JP-0.5B-init
- 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
The following checkpoints are initialized weights further pre-trained on hotchpotch/fineweb-2-edu-japanese for roughly 10B tokens with 1,024-token context lengths.
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}
}