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This model was released on 2022-07-27 and added to Hugging Face Transformers on 2022-09-14.

GPT-NeoX-Japanese

GPT-NeoX-Japanese, a Japanese language model based on GPT-NeoX. Japanese uses three types of characters (hiragana, katakana, kanji) and has a huge vocabulary. This model uses BPEEncoder V2, a sub-word tokenizer to handle the different characters.

The model also removes some bias parameters for better performance.

You can find all the original GPT-NeoX-Japanese checkpoints under the ABEJA organization.

[!TIP] This model was contributed by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori from ABEJA, Inc..

Click on the GPT-NeoX-Japanese models in the right sidebar for more examples of how to apply GPT-NeoX-Japanese to different language tasks.

The example below demonstrates how to generate text with [Pipeline] or the [AutoModel], and from the command line.

python

from transformers import pipeline


pipeline = pipeline(task="text-generation",
                    model="abeja/gpt-neox-japanese-2.7b", device=0)
pipeline("人とAIが協調するためには、")

</hfoption> <hfoption id="AutoModel">

python

from transformers import AutoModelForCausalLM, AutoTokenizer


model = AutoModelForCausalLM.from_pretrained("abeja/gpt-neox-japanese-2.7b", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b")
input_ids = tokenizer("人とAIが協調するためには、", return_tensors="pt").input_ids.to(model.device)
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

</hfoption> </hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to only quantize the weights to 4-bits.

python

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig


quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16"
)
model = AutoModelForCausalLM.from_pretrained(
    "abeja/gpt-neox-japanese-2.7b",
    quantization_config=quantization_config,
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b")
input_ids = tokenizer.encode("人とAIが協調するためには、", return_tensors="pt").to(model.device)
output = model.generate(input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Use the AttentionMaskVisualizer to better understand what tokens the model can and cannot attend to.

python

from transformers.utils.attention_visualizer import AttentionMaskVisualizer


visualizer = AttentionMaskVisualizer("abeja/gpt-neox-japanese-2.7b")
visualizer("What is shown in this image?")

Resources

Refer to the Training a better GPT model: Learnings from PaLM blog post for more details about how ABEJA trained GPT-NeoX-Japanese.

GPTNeoXJapaneseConfig

[[autodoc]] GPTNeoXJapaneseConfig

GPTNeoXJapaneseTokenizer

[[autodoc]] GPTNeoXJapaneseTokenizer

GPTNeoXJapaneseModel

[[autodoc]] GPTNeoXJapaneseModel - forward

GPTNeoXJapaneseForCausalLM

[[autodoc]] GPTNeoXJapaneseForCausalLM - forward