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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](./gpt_neox).
Japanese uses three types of characters (hiragana, katakana, kanji) and has a huge vocabulary. This model uses [BPEEncoder V2](https://github.com/tanreinama/Japanese-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](https://huggingface.co/abeja/models?search=gpt-neo-x) organization.

> [!TIP]
> This model was contributed by [Shinya Otani](https://github.com/SO0529), [Takayoshi Makabe](https://github.com/spider-man-tm), [Anuj Arora](https://github.com/Anuj040), and [Kyo Hattori](https://github.com/go5paopao) from [ABEJA, Inc.](https://www.abejainc.com/).
>
> 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.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline
pipeline = pipeline(task="text-generation", 
                    model="abeja/gpt-neox-japanese-2.7b", dtype=torch.float16, device=0)
pipeline("人とAIが協調するためには、")
```

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

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("abeja/gpt-neox-japanese-2.7b", dtype=torch.float16, 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>
<hfoption id="transformers CLI">
```bash
echo -e "人とAIが協調するためには、" | transformers run --task text-generation --model abeja/gpt-neox-japanese-2.7b --device 0
```

</hfoption>
</hfoptions>

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

The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to 4-bits.

```py
import torch
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](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to.

```py
from transformers.utils.attention_visualizer import AttentionMaskVisualizer

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

<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/gpt_neox_japanese-attn-mask.png"/>
</div>

## Resources
Refer to the [Training a better GPT model: Learnings from PaLM](https://medium.com/ml-abeja/training-a-better-gpt-2-93b157662ae4) 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
