In this article, we will guide you on how to effectively use the Japanese GPT-NeoX 3.6 billion parameter model, specially designed for instruction-following tasks. We will also cover troubleshooting tips to help you make the most of this advanced AI tool.
Overview of the Japanese GPT-NeoX Model
The Japanese GPT-NeoX is a powerful conversational agent pretrained with an extensive data set. This version, known as the instruction SFT (Supervised Fine-Tuning), has been tailored to enhance performance compared to its predecessor.
- Model Architecture: A transformer-based language model comprising 36 layers and a hidden size of 2816.
- SFT Comparison: An evaluation conducted with ChatGPT on 100 prompts demonstrated the improved capabilities of this model.
Preparing Your Inputs
To start generating responses, you need to format your prompts properly. Each conversation should alternate between the two speakers: ユーザー (User) and システム (System).
Think of your input prompt as setting a stage for a play where each character has a script to follow.
prompt = [
speaker: ユーザー,
text: コンタクトレンズを慣れるにはどうすればよいですか？
speaker: システム,
text: これについて具体的に説明していただけますか？何が難しいのでしょうか？
speaker: ユーザー,
text: 目が痛いのです。
speaker: システム,
text: 分かりました、コンタクトレンズをつけると目がかゆくなるということですね。思った以上にレンズを外す必要があるでしょうか？
speaker: ユーザー,
text: いえ、レンズは外しませんが、目が赤くなるんです。
]
prompt = [futtr[speaker]: uttr[text] for uttr in prompt]
prompt = NL.join(prompt)
prompt = (prompt + NL + システム:)
print(prompt)
# ユーザー: コンタクトレンズを慣れるにはどうすればよいですか？NL
# システム: これについて具体的に説明していただけますか？何が難しいのでしょうか？NL
# ユーザー: 目が痛いのです。NL
# システム: 分かりました、コンタクトレンズをつけると目がかゆくなるということですね。思った以上にレンズを外す必要があるでしょうか？NL
# ユーザー: いえ、レンズは外しませんが、目が赤くなるんです。NL
# システム:
Implementation Steps
Here’s how to implement and run the model using Python:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("rinnajapanese-gpt-neox-3.6b-instruction-sft-v2", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("rinnajapanese-gpt-neox-3.6b-instruction-sft-v2")
if torch.cuda.is_available():
model = model.to("cuda")
token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
with torch.no_grad():
output_ids = model.generate(
token_ids.to(model.device),
do_sample=True,
max_new_tokens=128,
temperature=0.7,
repetition_penalty=1.1,
pad_token_id=tokenizer.pad_token_id,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id
)
output = tokenizer.decode(output_ids.tolist()[0][token_ids.size(1):])
output = output.replace("NL", "\n")
print(output)
In this setup, you’re essentially loading a soft-spoken, yet highly capable assistant who is ready to respond to queries based on the context you provide.
Tokenization Technique
The model employs a sophisticated tokenizer that efficiently processes Japanese text, ensuring accuracy in understanding and generating responses.
print(tokenizer.tokenize("吾輩は猫である")) # [吾, 輩, は, 猫, である]
Troubleshooting Tips
If you encounter any issues while using the model, here are a few troubleshooting suggestions:
- Ensure that your inputs are formatted correctly, as improper prompts can lead to errors.
- Check your Python environment to confirm that all packages are correctly installed.
- For performance-related issues, consider verifying that your hardware meets the model’s requirements.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
With the Japanese GPT-NeoX model, you now have a powerful tool to enhance your conversational AI applications. By following the guidelines and leveraging the provided troubleshooting tips, you can effectively implement this model for your projects.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
