Llama 3.1 Korean Bllossom 405B
Llama 3.1 Korean Bllossom 405B is a powerful Korean-English bilingual language model that enhances the connection of knowledge between the two languages. What makes this model unique is its ability to improve Korean performance by 5-10% compared to its predecessor, while maintaining the same level of English performance. It's designed to generate more natural and friendly Korean sentences, making it a valuable tool for those who need to communicate in both languages. With its advanced architecture and training on a large dataset, this model achieves performance similar to or slightly lower than GPT-4. What sets it apart is its efficiency, allowing for faster and more accurate results, making it a practical choice for various applications.
Table of Contents
Model Overview
Meet Bllossom, a Korean-English bilingual language model that’s here to change the game! Based on the open-source LLama3.1, Bllossom enhances the connection between Korean and English knowledge. But what makes it so special?
Key Features
- Improved Korean performance: Bllossom outperforms ==Llama 3.1-405B-Inst== by 5-10% in single-turn evaluations.
- Complete bilingual model: It doesn’t compromise on English performance, making it a perfect blend of both languages.
- Natural and friendly Korean sentences: Bllossom generates more human-like Korean sentences compared to existing models.
- Similar to GPT-4: Human evaluations and GPT evaluations show Bllossom’s performance is similar to or slightly lower than GPT-4.
What’s behind Bllossom?
- Collaboration: Developed by MLPLab at Seoultech, Teddysum, and Yonsei Univ, with support from HP and Common Crawl.
- Advanced technology: Uses lightweight pre-training techniques, instruction tuning, and RAG technology.
What can you do with Bllossom?
- Create your own model: With just
6 A100 GPUs, you can build your own model using Bllossom. - Quantization model: If you have limited GPU resources, you can use the quantization model with
3 A100 GPUsor4 A6000 GPUs.
Example Use Cases
- Tourism: Generate a popular tourist course in Seoul, including historical and cultural attractions
- Language Translation: Translate text from Korean to English and vice versa
- Chatbot: Use the model as a helpful AI assistant to answer user questions kindly
Performance
Bllossom is a powerful Korean-English bilingual language model that showcases remarkable performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
Bllossom is built on the open-source LLama3.1 and has been optimized for faster processing. With the support of A100 computing power, it can handle large-scale datasets with ease. But how fast is it, exactly? Imagine processing 1.8M pixels in a matter of seconds!
Accuracy
When it comes to accuracy, Bllossom stands out from the competition. It has achieved a 5-10% improvement in Korean performance compared to ==Llama 3.1-405B-Inst==. But what does this mean in real-world scenarios? For instance, if you ask Bllossom to generate a Seoul tourist course, it can provide a more accurate and helpful response.
Efficiency
Bllossom is designed to be efficient, making it accessible to a wide range of users. With 6 A100 GPUs, you can create your own model using Bllossom. But what if you don’t have access to that many GPUs? Don’t worry! You can still utilize Bllossom with 3 A100 GPUs or 4 A6000 GPUs by leveraging its quantization model.
Limitations
While Bllossom has shown impressive performance in generating natural and friendly Korean sentences, there are still some limitations to be aware of.
Limited Context Understanding
The model’s ability to understand context is limited to the input prompt and may not always capture the nuances of human communication. For example, if the user asks a follow-up question that relies on previous context, the model may not be able to understand the connection.
Dependence on Training Data
The model’s performance is heavily dependent on the quality and diversity of the training data. If the training data is biased or limited in scope, the model’s output may reflect these biases.
Format
Bllossom is a Korean-English bilingual language model based on the open-source LLama3.1. It has a transformer architecture and supports input in the form of tokenized text sequences.
Input Format
The model accepts input in the form of a single string or a list of strings, where each string represents a sentence or a piece of text. The input text should be pre-processed to include a specific prompt that indicates the start of the conversation.
Here’s an example of how to format the input:
PROMPT = "You are a helpful AI assistant. Please answer the user's questions kindly."
instruction = "서울의 유명한 관광 코스를 만들어줄래?"
messages = [
{"role": "system", "content": PROMPT},
{"role": "user", "content": instruction}
]
Output Format
The model generates output in the form of a string, which represents the response to the user’s input. The output is generated based on the input prompt and the conversation history.
Here’s an example of how to handle the output:
outputs = pipeline(prompt, max_new_tokens=2048, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9)
print(outputs[0]["generated_text"][len(prompt):])
Special Requirements
The model requires a specific set of dependencies to be installed, including torch and transformers. It also requires a GPU with at least 6 A100 cards to run efficiently.
Here’s an example of how to install the dependencies:
pip install torch transformers==4.40.0 accelerate
