Welcome to your guide on utilizing the Breeze-7B language model series by MediaTek Research! This family of models has been tailored specifically for Traditional Chinese, providing a robust tool for a variety of applications including question-answering, chat, and summarization. In this blog, you will learn the practical steps to implement Breeze-7B, along with troubleshooting tips for potential hiccups along the way.

Understanding Breeze-7B Models

The Breeze-7B lineup includes several models, each designed to cater to different needs:

• Breeze-7B-Base: The foundational model with an extended vocabulary tailored for Traditional Chinese.
• Breeze-7B-Instruct: A model derived from Breeze-7B-Base that is ready for immediate use in common tasks.
• Breeze-7B-Instruct-64k: An upgraded version that accommodates a context length of 64k tokens for long content processing.

Installing Dependencies

Before diving into model usage, you need to set up your environment. Follow these steps:

pip install transformers torch accelerate

If you’re interested in faster inference using flash-attention2, install the following:

pip install packaging ninja
pip install flash-attn

Loading the Model

With your environment ready, it’s time to load the model in your code. Here’s how you can do it:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "MediaTek-Research/Breeze-7B-Instruct-v0_1",
    device_map="auto",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2" # optional
)

Think of this process like preparing a chef’s kitchen: thedependencies are your kitchen tools, loading the model is like pulling out your ingredients, getting ready to create culinary masterpieces for your linguistically inclined audience!

Structuring Your Queries

The well-structured queries are crucial for successful interactions with the model. Here’s a sample format you can follow:

SYS_PROMPT = "You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan."
QUERY1 = "What is AI?"
RESPONSE1 = ""
QUERY2 = "Tell me about Breeze-7B."

Applying Chat Templates

For seamless conversation flow, you can utilize chat templates:

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("MediaTek-Research/Breeze-7B-Instruct-v0_1")
chat = [
    {"role": "user", "content": ""},
    {"role": "assistant", "content": ""},
]
tokenized_chat = tokenizer.apply_chat_template(chat, tokenize=False)

Troubleshooting Common Issues

Even the best-laid plans can encounter a few bumps. Here are some troubleshooting tips to address common issues:

• Installation Errors: Ensure you are using a compatible Python version and have installed all dependencies properly.
• Model Loading Issues: Double-check the model path and ensure that your internet connection is stable for downloading.
• Slow Performance: If the model runs slow, consider switching to a more powerful machine or optimizing your code.

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

Conclusion

With the Breeze-7B series, you have a powerful tool at your fingertips for tackling a wide range of language tasks. By following the provided guidelines and utilizing the practical tips shared above, you will be well on your way to mastering this language model.

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.