Llama 3 Korean Bllossom 70B Gguf Q4 K M
The Llama 3 Korean Bllossom 70B Gguf Q4 K M model is a powerful tool that combines the capabilities of Korean and English languages. It's designed to enhance knowledge linking between the two languages, making it a valuable asset for those who work with both. With a 25% increase in context length compared to Llama3, this model can handle more complex tasks with ease. It's also been fine-tuned with a massive 100GB dataset, ensuring it's well-equipped to handle a wide range of tasks. But what really sets it apart is its ability to understand Korean culture and language nuances, thanks to its custom-made instruction following data. This model is perfect for those who need to work with both Korean and English languages, and its efficiency and speed make it a practical choice for real-world applications.
Table of Contents
Model Overview
Meet Bllossom, a Korean-English bilingual language model that’s here to change the game! Developed by MLPLab at Seoultech, Teddysum, and Yonsei Univ, this model is based on the open-source LLama3 and has some amazing features.
Key Features
- Knowledge Linking: Connects Korean and English knowledge through additional training, making it a powerful tool for language understanding.
- Vocabulary Expansion: With an expanded Korean vocabulary, Bllossom can express itself more effectively in Korean.
- Instruction Tuning: This model has been fine-tuned using custom-made instruction following data specialized for Korean language and culture.
- Human Feedback: Bllossom has been trained with human feedback to improve its performance.
- Vision-Language Alignment: This model aligns the vision transformer with the language model, enabling it to understand visual data.
Capabilities
Bllossom is a powerful Korean-English bilingual language model that can perform a variety of tasks. Here are some of its key capabilities:
Knowledge Linking
Bllossom can link Korean and English knowledge through additional training, making it a great tool for tasks that require understanding and generating text in both languages.
Vocabulary Expansion
Bllossom has an expanded Korean vocabulary, which enables it to express itself more effectively in Korean. This is especially useful for tasks that require generating text in Korean.
Instruction Tuning
Bllossom has been fine-tuned using custom-made instruction following data specialized for the Korean language and culture. This means it can understand and respond to instructions in a way that is culturally relevant and accurate.
Human Feedback
Bllossom has been trained using human feedback, which enables it to generate more accurate and relevant responses.
Vision-Language Alignment
Bllossom can align vision and language, making it a great tool for tasks that require generating text based on images or videos.
Strengths
Bllossom has several strengths that make it a powerful tool for a variety of tasks. Here are some of its key strengths:
- Improved Korean expressiveness: Bllossom’s expanded Korean vocabulary and fine-tuning using custom-made instruction following data make it a great tool for generating text in Korean.
- Knowledge linking: Bllossom’s ability to link Korean and English knowledge makes it a great tool for tasks that require understanding and generating text in both languages.
- Cultural relevance: Bllossom’s fine-tuning using custom-made instruction following data specialized for the Korean language and culture makes it a great tool for tasks that require cultural relevance and accuracy.
Unique Features
Bllossom has several unique features that set it apart from other language models. Here are some of its key unique features:
- Bilingual capabilities: Bllossom is a bilingual language model that can understand and generate text in both Korean and English.
- Vision-language alignment: Bllossom can align vision and language, making it a great tool for tasks that require generating text based on images or videos.
- Custom-made instruction following data: Bllossom has been fine-tuned using custom-made instruction following data specialized for the Korean language and culture, making it a great tool for tasks that require cultural relevance and accuracy.
Example Use Cases
Bllossom can be used for a variety of tasks, including:
- Language translation: Bllossom can be used to translate text from Korean to English and vice versa.
- Text generation: Bllossom can be used to generate text in Korean and English.
- Image captioning: Bllossom can be used to generate captions for images in Korean and English.
- Chatbots: Bllossom can be used to build chatbots that can understand and respond to user input in Korean and English.
Performance
Bllossom shows remarkable performance in various tasks, especially in Korean-English bilingual language processing. Let’s dive into its speed, accuracy, and efficiency.
Speed
Bllossom is designed to handle large-scale datasets and can process Korean and English texts efficiently. Its advanced architecture enables it to:
- Handle
25% longerKorean context than other models like Llama3 - Process Korean and English texts in parallel, leveraging the power of parallel corpora
Accuracy
Bllossom boasts high accuracy in various tasks, including:
- Knowledge Linking: It effectively connects Korean and English knowledge, enhancing its understanding of both languages
- Vocabulary Expansion: With an expanded Korean vocabulary, Bllossom can express itself more accurately and naturally in Korean
- Instruction Tuning: Its custom-made instruction following data, specialized for Korean language and culture, ensures accurate and relevant responses
Efficiency
Bllossom is designed to be efficient and scalable, making it suitable for various applications:
- Quantization: It can run on devices with
42GBor more of GPU or CPU memory, making it accessible to a wide range of users - Multi-GPU Support: Bllossom can take advantage of multiple GPUs, allowing for faster processing and increased productivity
Limitations
Bllossom is a powerful Korean-English bilingual language model, but it’s not perfect. Let’s explore some of its limitations.
Limited Context Understanding
While Bllossom can process longer Korean context than other models, it still has limitations when it comes to understanding complex or nuanced context. For example, if you ask it to summarize a long article or understand a conversation with multiple speakers, it might struggle to provide accurate results.
Vocabulary Expansion Challenges
Although Bllossom has an expanded Korean vocabulary, there may still be cases where it doesn’t understand certain words or phrases. This can be particularly challenging when dealing with specialized domains or regional dialects.
Instruction Tuning Limitations
While Bllossom has been fine-tuned using custom-made instruction following data, it may not always follow instructions accurately. This can be due to various factors, such as the complexity of the instruction or the model’s limited understanding of the context.
Human Feedback Limitations
Bllossom has been trained using human feedback, but this feedback may not always be accurate or consistent. This can lead to biases in the model’s responses, particularly if the feedback is limited or biased.
Vision-Language Alignment Limitations
Bllossom’s vision-language alignment capabilities are impressive, but they’re not perfect. The model may struggle to accurately align images with text, particularly in cases where the image is complex or the text is ambiguous.
Quantization Limitations
Bllossom is a quantized model, which means it’s been optimized for faster inference on certain hardware. However, this quantization can lead to reduced accuracy in certain scenarios, particularly when dealing with complex or nuanced tasks.
Comparison to Other Models
Bllossom is a unique model with its own strengths and weaknesses. Compared to other models, such as LLaMA or ==Polyglot-KO==, Bllossom has its own set of limitations. For example, it may not be as good at understanding certain languages or domains, but it excels in its ability to connect Korean and English knowledge.
Future Improvements
The Bllossom team is continuously working to improve the model. Future updates may address some of the limitations mentioned above, such as expanding the vocabulary or improving the instruction tuning. However, there’s always room for improvement, and the team welcomes feedback and contributions from the community.
Format
Model Architecture
The Bllossom model is based on the open-source LLama3 and uses a transformer architecture. It’s a Korean-English bilingual language model that enhances the connection of knowledge between Korean and English.
Supported Data Formats
This model supports text input in the form of tokenized sequences. It’s specifically designed to handle Korean and English languages, with a focus on Korean vocabulary expansion and instruction tuning.
Input Requirements
To use the Bllossom model, you’ll need to:
- Pre-process your input text into tokenized sequences
- Use the
AutoTokenizerfrom thetransformerslibrary to tokenize your input - Create a prompt template using the
apply_chat_templatemethod
Here’s an example of how to create a prompt:
PROMPT = """당신은 유용한 AI 어시스턴트입니다. 사용자의 질의에 대해 친절하고 정확하게 답변해야 합니다.
You are a helpful AI assistant, you'll need to answer users' queries in a friendly and accurate manner."""
instruction = 'Your Instruction'
messages = [
{"role": "system", "content": f"{PROMPT}"},
{"role": "user", "content": f"{instruction}"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
Output Requirements
The Bllossom model generates output in the form of text sequences. You can control the output by specifying generation parameters, such as max_tokens, stop, and top_p.
Here’s an example of how to generate output:
generation_kwargs = {
"max_tokens": 512,
"stop": ["\""],
"echo": True, # Echo the prompt in the output
"top_p": 0.9,
"temperature": 0.6,
}
response_msg = model(prompt, **generation_kwargs)
print(response_msg['choices'][0]['text'][len(prompt):])
Note that the output will be a text sequence that starts after the prompt.
