In the evolving world of artificial intelligence, KULLM3 stands out as a robust model adept in instruction-following and engaging conversations in Korean. In this guide, we’ll walk you through the steps to utilize KULLM3 in your projects, giving you the tools to harness its capabilities effectively.

Introduction to KULLM3

KULLM3 is developed by NLPAI Lab and features advanced instruction-following ability, rivaling even the renowned gpt-3.5-turbo. This model is particularly noteworthy for its proficiency in Korean, making it one of the best publicly available Korean language models.

Getting Started with KULLM3

Before diving into coding with KULLM3, you’ll need to install some dependencies and understand the basic programming workflow. Here’s how to do it:

1. Install Dependencies

To begin, you’ll need to set up your environment. Open your command line interface and execute the following command:

bash
pip install torch transformers==4.38.2 accelerate

Note: As of version 4.39.0, you may encounter issues with the generate() function. It’s recommended to use 4.38.2 for stability as of April 2024.

2. Load the KULLM3 Model in Python

Now that you have installed the necessary tools, you can load the model using the following Python code:

python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

MODEL_DIR = "nlpai-lab/KULLM3"
model = AutoModelForCausalLM.from_pretrained(MODEL_DIR, torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

s = "고려대학교에 대해서 알고 있니?" # Asking about Korea University
conversation = [{"role": "user", "content": s}]
inputs = tokenizer.apply_chat_template(
    conversation,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

_ = model.generate(inputs, streamer=streamer, max_new_tokens=1024)
# Example response about 고려대학교

An Analogy for Understanding the Code

Imagine that KULLM3 is like a highly skilled tour guide (the model) equipped with a sophisticated map (the tokenizer). When you ask a question (your input), the guide references the map to determine the best route to provide you with the information (generate a response). The process involves the guide actively listening and using the map, which represents the training data and instructions, to navigate effectively to your desired answer.

Training Details

The training of KULLM3 involved a diverse dataset containing over 66,000 examples, capturing various forms of Korean instructions, both crafted and generated. This extensive dataset ensures the model’s conversations are not only accurate but also contextually relevant.

Troubleshooting

If you encounter any issues while using KULLM3, consider the following troubleshooting tips:

• Ensure that you are using the correct version of the transformers library to avoid incompatibility issues.
• Check your CUDA installation if you face problems related to GPU usage.
• If the model generates unexpected outputs, reassess the conversation prompts to ensure clarity.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

With KULLM3 in your toolkit, the doors to advanced conversational AI in Korean are wide open. This model offers a wealth of possibilities for developers looking to harness the power of language processing technology.

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.