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transformers>=4.51.0
accelerate>=1.6.0
sentencepiece>=0.2.0
flash-attn>=2.7.31import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4model_name = "iamtatsuki05/Llama-JP-0.5B-init"
5model_kwargs = {
6 "torch_dtype": torch.bfloat16,
7 "attn_implementation": "flash_attention_2",
8 "device_map": "auto",
9}
10tokenizer = AutoTokenizer.from_pretrained(model_name)
11model = AutoModelForCausalLM.from_pretrained(model_name, **model_kwargs)
12
13prompt = "ちいかわのハチワレは"
14inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
15output = model.generate(
16 **inputs,
17 max_new_tokens=256,
18 temperature=0.8,
19 top_p=0.9,
20 do_sample=True,
21)
22print(tokenizer.decode(output[0], skip_special_tokens=True))| 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 |
1@article{MIREI
2 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},
3 author={岡田 龍樹 and 杉本 徹},
4 journal={言語処理学会第 32 回年次大会 (NLP2026)},
5 year={2026}
6}