How to Utilize the Breeze-7B-Instruct Language Model

Oct 28, 2024 | Educational

The Breeze-7B-Instruct model, developed by MediaTek Research, is designed for text generation tasks with a focus on Traditional Chinese. Below, you will find a step-by-step guide on how to set up and utilize this powerful language model in your projects.

Getting Started with Breeze-7B-Instruct

To use the Breeze-7B-Instruct model effectively, you will need to follow these key steps:

  1. Install Required Libraries:

    Before you can use the model, you need to set up your environment with the appropriate libraries. Run the following commands:

    pip install transformers torch accelerate
  2. Optional for Faster Inference:

    If you’re aiming for improved performance, especially during inference, you can install flash-attention as follows:

    pip install packaging ninja
    pip install flash-attn

Loading the Model

Once you have installed the necessary dependencies, you can load the Breeze-7B-Instruct model as shown below:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load model
model = AutoModelForCausalLM.from_pretrained(
    "MediaTek-Research/Breeze-7B-Instruct-v1_0",
    device_map="auto",
    torch_dtype=torch.bfloat16
)

Using the Model for Text Generation

The Breeze-7B-Instruct has a specific structure for input queries. Think of it like a script for a performance where different actors have defined roles. Here is how you can format your queries:

query_structure = [
    "SYS_PROMPT",
    "[INST] QUERY1 [INST] RESPONSE1 [INST]",
    "[INST] QUERY2 [INST]"
]

For implementation, you would define your SYS_PROMPT, which sets the context for the AI, along with your queries. Imagine it as a director guiding the actors from backstage.

Generating Text Outputs

After setting up your queries, you can generate text outputs with the model:

outputs = model.generate(
    tokenizer.apply_chat_template(chat, return_tensors="pt"), 
    max_new_tokens=128,
    top_p=0.01,
    top_k=85,
    repetition_penalty=1.1,
    temperature=0.01
)

Here, you provide parameters that control the parameters of the generation, such as the creativity and length of the text.

Troubleshooting

If you encounter issues while working with the Breeze-7B-Instruct model, consider the following troubleshooting tips:

  • Environment Configuration: Ensure that your Python environment is set up correctly with all necessary packages installed. A common issue is missing dependencies.
  • Device Compatibility: Verify that you are using compatible hardware for running the model, especially when using GPU settings.
  • Token Length Errors: If you get errors related to input length, ensure that your input does not exceed the maximum token limit (8k). Consider shortening your queries.

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

Conclusion

By following the steps outlined in this guide, you can effectively harness the capabilities of the Breeze-7B-Instruct model for your text generation tasks. With its enhanced vocabulary and refined training, it is an excellent resource for both Traditional Chinese and English applications.

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.

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