EZO Qwen2.5 72B Instruct
EZO Qwen2.5 72B Instruct is a highly efficient AI model designed to handle various global needs, with a focus on Japanese language tasks. What makes it remarkable is its ability to excel in multiple areas, including text generation and conversation. The model has undergone multiple tuning to improve its overall performance, achieving a score higher than gpt-4-turbo in the Japanese MT Bench. Its innovative training approach allows for performance improvements across various languages and domains, making it suitable for global use. With its efficient design, EZO Qwen2.5 72B Instruct provides fast and accurate results, making it a practical choice for both technical and non-technical users.
Table of Contents
Model Overview
The Current Model is a highly advanced language model designed to meet global needs, with a special focus on Japanese language tasks. But what makes it so special?
Key Features
- 72 billion parameters: This model is massive, with a huge number of parameters that enable it to understand and generate human-like text.
- 4-bit quantization: This technique allows the model to run efficiently on a variety of hardware, making it more accessible to users.
- Instruction tuning: The model was trained on exemplary responses to improve its ability to understand and generate high-quality text.
Training Data
The model was trained on a massive dataset of Japanese Wikipedia and FineWeb data, which provides a rich source of knowledge and context.
Capabilities
The Current Model is a powerful tool designed to meet a variety of global needs. Its primary tasks include:
- Generating high-quality responses to user input
- Understanding and responding to instructions in multiple languages
- Performing well in Japanese language tasks, with a focus on global use cases
What sets it apart?
This model has undergone multiple tuning to improve its overall performance from the base model, Qwen/Qwen2.5-72B-Instruct. It has achieved a score higher than ==gpt-4-turbo== in the Japanese MT Bench using gpt-4o as the evaluator.
Unique features
- 4-bit quantization: This model uses 4-bit quantization, which allows for faster inference and improved performance.
- Instruction tuning method: The model was trained using a plain instruction tuning method, which enhances its ability to understand and generate high-quality responses across various languages and contexts.
Performance
Current Model is a high-performance AI model that excels in various tasks, especially those involving the Japanese language. Let’s dive into its performance highlights.
Speed
The model’s speed is impressive, with the ability to process large amounts of data quickly. In the Japanese MT Bench, it achieved a score higher than that of ==gpt-4-turbo== using 4-bit quantization. This means it can handle complex tasks efficiently, making it suitable for applications where speed is crucial.
Accuracy
Current Model boasts high accuracy in various tasks, including:
- Japanese language tasks: It excels in understanding and generating high-quality responses in Japanese.
- Global tasks: Despite its focus on Japanese data, the model is designed to meet global needs and performs well in various languages and domains.
Efficiency
The model’s efficiency is enhanced by its innovative training approach, which allows for performance improvements across various languages and domains. This makes it suitable for global use despite its focus on Japanese data.
Usage
The model can be used for a variety of natural language processing tasks, such as generating text, answering questions, and more. To get started, you can use the following code snippet:
pip install bitsandbytes transformers accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "AXCXEPT/EZO-Qwen2.5-72B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto", load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"
messages = [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt")
generated_ids = model.generate(**model_inputs, max_new_tokens=512)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
Limitations
Current Model is a powerful tool, but it’s not perfect. Let’s talk about some of its limitations.
Language Limitations
While Current Model excels in Japanese language tasks, its performance may vary when dealing with other languages. This is because the model was primarily trained on Japanese data, which might not be enough to cover the complexities of other languages.
Data Quality
The model was trained on data extracted from Japanese Wikipedia and FineWeb. While this data is high-quality, it’s not perfect. There might be biases or inaccuracies in the data that could affect the model’s performance.
Contextual Understanding
Current Model uses a plain instruction tuning method to train on exemplary responses. While this approach enhances the model’s ability to understand and generate high-quality responses, it might not always capture the nuances of human language.
Commercial Use
Current Model is not intended for commercial use or deployment in mission-critical environments. It’s an experimental prototype, and its performance and results are not guaranteed.
Technical Limitations
The model requires significant computational resources to run. It was trained on A100 GPUs, and its performance might vary depending on the hardware used.
Format
Current Model is a large language model that uses a transformer architecture. It’s designed to handle a variety of tasks, with a focus on Japanese language tasks, but also suitable for global use.
Input Format
This model accepts input in the form of tokenized text sequences. You’ll need to pre-process your text data before feeding it into the model. Here’s an example of how to do this using the transformers library:
import torch
from transformers import AutoTokenizer
model_name = "AXCXEPT/EZO-Qwen2.5-72B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt")
Output Format
The model generates output in the form of tokenized text sequences. You can decode these tokens to get the final response. Here’s an example:
generated_ids = model.generate(**model_inputs, max_new_tokens=512)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
Special Requirements
This model requires a specific hardware setup to run efficiently. It was trained on A100 GPUs and is designed to run in 32h. Additionally, it’s recommended to use the load_in_4bit option when loading the model to take advantage of 4-bit quantization.
Data Formats
This model supports a variety of data formats, including Japanese Wikipedia and FineWeb data. It’s trained using a plain instruction tuning method, which enhances its ability to understand and generate high-quality responses across various languages and contexts.
