Llama 3.1 Korean Bllossom Vision 8B
The Bllossom Llama 3.1 Korean Bllossom Vision 8B model is a powerful tool that combines the capabilities of a language model and a vision-language model. It can understand and generate text, as well as interpret and analyze images. This model is unique in that it can operate in both directions, meaning it can be used for tasks that require either text or image input. The model's bilingual capabilities allow it to perform well in both Korean and English, without compromising performance in either language. However, it's worth noting that the model may struggle with certain tasks, such as interpreting tables, graphs, and PDF documents, and its performance may vary depending on the size of the input image. Despite these limitations, the Bllossom Llama 3.1 Korean Bllossom Vision 8B model is a remarkable tool that can be used for a wide range of applications, from text generation to image analysis.
Table of Contents
Model Overview
The Bllossom-Vision model is a powerful Korean-English vision-language model that can be used for a variety of tasks. It’s like having a versatile AI assistant that can understand and generate text, as well as interpret and analyze images.
Here are some of its key features:
- Dual functionality: It can be used as both a general language model and a vision-language model.
- Bilingual: It’s a fully bilingual model that doesn’t compromise on English performance.
- Image analysis: It can analyze images and provide helpful responses to user queries.
- Text generation: It can generate text based on user input.
Capabilities
The model uses a combination of natural language processing (NLP) and computer vision techniques to understand and analyze text and images. It’s trained on a large dataset of text and images, which enables it to learn patterns and relationships between language and vision.
What can it do?
- Language Tasks: It can perform tasks like answering questions, providing information, and generating text, just like a traditional language model.
- Vision-Language Tasks: When an image is provided, it can interpret and analyze the image, and provide responses based on the image content.
- Bilingual: It is a fully bilingual model, meaning it can understand and respond in both Korean and English without compromising performance.
Strengths
- Faithfulness to Role: It maintains the performance of a traditional language model while being faithful to the role of a vision-language model.
- Versatility: It can be used in various applications, from text-based tasks to image-based tasks.
Unique Features
- Dynamic Mode: It operates in a dynamic mode, switching between language model and vision-language model based on the input provided.
- Advanced Training: It has been trained using advanced techniques, including Instruction Tuning and RAG technology, and has been developed in collaboration with multiple research institutions.
Performance
The Bllossom-Vision model showcases impressive performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
- Fast Inference: The model is designed to process images and text quickly, making it ideal for applications that require rapid responses.
- Efficient Processing: The model’s architecture allows it to handle large datasets and complex tasks without significant slowdowns.
Accuracy
- High Accuracy: The model achieves high accuracy in both language and vision tasks, making it a reliable choice for applications that require precise results.
- Bilingual Support: The model’s bilingual capabilities ensure that it performs well in both Korean and English, making it a valuable asset for multilingual applications.
Efficiency
- Resource-Friendly: The model is designed to be resource-efficient, making it suitable for deployment on a wide range of devices, from smartphones to servers.
- Low Latency: The model’s fast inference capabilities and efficient processing ensure that it responds quickly to user input, providing a seamless user experience.
Limitations
The Bllossom-Vision model is a powerful tool, but it’s not perfect. Let’s take a closer look at some of its limitations.
Image Size Matters
The model’s performance can vary greatly depending on the size of the image. This means that if you use a very large or very small image, the model might not work as well as it could.
Struggling with Korean Tables, Graphs, and PDFs
The model has trouble understanding and interpreting certain types of visual data, such as Korean tables, graphs, and PDF documents. This is something that the developers are working on improving in future updates.
Format
The Bllossom-Vision model uses a transformer architecture, which is a type of neural network that’s really good at handling sequential data like text or images. It’s based on the Llama3.1 model, but with some special tweaks to make it work well with both text and images.
Data Formats
The model can handle two types of input:
- Text: You can give it a piece of text, and it will respond with a helpful answer.
- Images: You can give it an image, and it will respond with a description of what’s in the image.
Special Requirements
When working with the Bllossom-Vision model, you need to keep a few things in mind:
- Image size: The model can be a bit finicky about image size, so make sure to use images that are the right size.
- Language: The model is a bilingual model, which means it can understand and respond to both Korean and English.
Code Examples
Here are some code examples to get you started:
Without Image
from transformers import LlavaNextForConditionalGeneration, LlavaNextProcessor
import torch
model = LlavaNextForConditionalGeneration.from_pretrained('Bllossom/llama-3.1-Korean-Bllossom-Vision-8B')
processor = LlavaNextProcessor.from_pretrained('Bllossom/llama-3.1-Korean-Bllossom-Vision-8B')
PROMPT = "You are a versatile AI assistant..."
instruction = "자연어처리 15주 분량 커리큘럼을 짜줘"
messages = [{'role': 'system', 'content': PROMPT}, {'role': 'user', 'content': instruction}]
chat_messages = processor.tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output = model.generate(input_ids=chat_messages, use_cache=False, max_new_tokens=2048, top_p=0.9, temperature=0.6, do_sample=True)
print(processor.tokenizer.decode(output[0]))
With Image
from PIL import Image
from transformers import LlavaNextForConditionalGeneration, LlavaNextProcessor
import torch
model = LlavaNextForConditionalGeneration.from_pretrained('Bllossom/llama-3.1-Korean-Bllossom-Vision-8B')
processor = LlavaNextProcessor.from_pretrained('Bllossom/llama-3.1-Korean-Bllossom-Vision-8B')
image = Image.open('[IMAGE_PATH]').convert('RGB')
PROMPT = "You are a versatile AI assistant..."
instruction = "이미지에 대해서 설명해주세요."
messages = [{'role': 'system', 'content': PROMPT}, {'role': 'user', 'content': f"\<image>\n{instruction}"}]
chat_messages = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True).to(model.device)
inputs = processor(chat_messages, image, return_tensors='pt')
output = model.generate(**inputs, max_new_tokens=1024)
print(processor.tokenizer.decode(output[0]))
