ABEJA-Qwen2.5-32b-Japanese-v0.1
ABEJA-Qwen2.5-32b-Japanese-v0.1はQwen/Qwen2.5-32B-Instructをベースに日本語中心とした継続事前学習を実施したモデルです。 Post-Traningはまだ実施しておらず、ChatVector(Qwen/Qwen2.5-32B-InstructとQwen/Qwen2.5-32B の差分ベクトル)により指示追従性能をあげています。
詳細はABEJAのテックブログを参照してください。
https://tech-blog.abeja.asia/entry/geniac2-qwen25-32b-v0.1
使い方
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "abeja/ABEJA-Qwen2.5-32b-Japanese-v0.1"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "人とAIが協調するためには？"
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
開発者
- Hiroshi Kiyota
- Keisuke Fujimoto
- Kentaro Nakanishi
- Kyo Hattori
- Shinya Otani
- Shogo Muranushi
- Takuma Kume
- Tomoki Manabe
(*)アルファベット順