Welcome to the world of advanced AI with the BOREA model, an exceptional technology built upon the Phi-3.5-mini-Instruct framework! This comprehensive guide will walk you through the setup and usage of this powerful model, enhancing your experience with text generation tasks. Whether you’re working with English or Japanese language data, BOREA is here to assist you!

Overview of the BOREA Model

The BOREA model is a general-purpose AI tool that has shown improved performance in text generation, particularly when it comes to processing the Japanese language. Thanks to multiple tuning techniques, this model is finely adapted to deliver better results than its base version.

How to Set Up the BOREA Model

Follow these step-by-step instructions to get started:

1. Begin by installing the necessary libraries. Open your terminal or command window, and run the following commands:

pip install flash_attn==2.5.8
pip install accelerate==0.31.0
pip install transformers==4.43.0
pip install -U trl
pip install pytest

Loading the Model Locally

Now that your environment is ready, you can load the BOREA model with the sample code below:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

torch.random.manual_seed(0)

model = AutoModelForCausalLM.from_pretrained(
    'HODACHIBorea-Phi-3.5-mini-Instruct-Coding',
    device_map='cuda',
    torch_dtype='auto',
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained('HODACHIBorea-Phi-3.5-mini-Instruct-Coding')

messages = [
    {"role": "system", "content": "AI"},
    {"role": "user", "content": "DNA"},
]

pipe = pipeline(
    'text-generation',
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    'max_new_tokens': 1024,
    'return_full_text': False,
    'temperature': 0.0,
    'do_sample': False,
}

output = pipe(messages, **generation_args)
print(output[0]['generated_text'])

The setup code can be thought of as preparing a garden before planting seeds. You first gather all the necessary tools and materials, like soil and seeds (libraries and models), and then you patiently plant each one in a structured way to ensure that they grow — similar to how we deploy the model with set parameters.

Tips for Usage

Recommended Guidelines:

• If you intend to use the model for commercial purposes, please inform Axcxept Co., Ltd. via email at info@axcxept.com for collaborative opportunities.
• Provide proper attribution when utilizing or modifying the model:

This project utilizes HODACHIBorea-Phi-3.5-mini-Instruct-Coding, a model based on Phi-3.5-mini-Instruct and fine-tuned by Axcxept Co., Ltd.

• Your feedback is welcome! Feel free to share insights via email at info@axcxept.com.

Troubleshooting Tips

While you’re exploring the BOREA model, you might face some issues along the way. Here are a few troubleshooting tips to help you out:

• Common Installation Issues: If you encounter errors while installing the required libraries, ensure that your Python environment is up to date. You might want to try using a virtual environment to avoid conflicts.
• Model Loading Errors: Double-check that you have the correct model name (‘HODACHIBorea-Phi-3.5-mini-Instruct-Coding’) typed without any typos.
• Inference Problems: If the model is not generating output as expected, review your input messages, and make sure they are formatted properly.

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

Conclusion

With the BOREA model, you can now enjoy enhanced text generation capabilities! Whether you aim to innovate in your projects, conduct research, or improve customer interactions, this powerful tool will help you achieve your goals.

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.