from transformers import AutoModelForCausalLM

model_name = "lightblue/japanese-mpt-7b"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype='auto',
    trust_remote_code=True
)

To run this model, you may need to load it in a lower precision in order for it to fit onto your GPU. We found for a T4 GPU, it requires loading the model in 8-bit precision. To load the model in 8-bit and 4-bit, please install the following pip packages:

pip install bitsandbytes accelerate

Caution - you will also need enough RAM to load the model. We estimate loading this model requires ~30GB.

Code to load the model in 8 bit

python

from transformers import AutoModelForCausalLM

model_name = "lightblue/japanese-mpt-7b"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype='auto',
    load_in_8bit=True,
    trust_remote_code=True
)

Code to load the model in 4 bit

python

from transformers import AutoModelForCausalLM

model_name = "lightblue/japanese-mpt-7b"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype='auto',
    load_in_4bit=True,
    trust_remote_code=True
)

How to use

python

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = """A: こんにちは
B: こんにちは
A: 好きなスポーツは何ですか？
B: サッカーです
A: 好きな食べ物は何ですか？
B:"""

pipe(prompt, temperature=0, do_sample=False, return_full_text=False, max_new_tokens=32)
# [{"generated_text": " カレーです
# A: 好きな色は何ですか？
# B: 赤です"}]