Welcome to your guide on effectively using the BOREA model, based on phi-3.5-mini-Instruct. This model has been specifically tailored to enhance performance, especially in the Japanese language. Follow this guide to seamlessly integrate the model into your projects!

Model Overview

The BOREA model is designed to support general-purpose applications. Imagine it as a Swiss Army knife of AI models—compact yet versatile, offering numerous functionalities. Just as a Swiss Army knife has tools for various tasks, the BOREA model is finely tuned to generate text across different contexts and languages, while boasting improved capabilities in Japanese.

Getting Started

To kick off your journey, you will need to install a few essential packages. Open your terminal and execute 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

After installing the necessary packages, you can load the model. Think of this step like setting up a new appliance in your kitchen; it’s the gateway to discovering new recipes (or in this case, generating text!). Use the following Python code:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

torch.random.manual_seed(0)

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

tokenizer = AutoTokenizer.from_pretrained("HODACHIBorea-Phi-3.5-mini-Instruct-Common")

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'])

Understanding the Code

Let’s break that down with an analogy: Think of the code above like preparing a meal. First, you gather your ingredients (importing the necessary libraries), then you select the recipe (loading the model). Once your ‘ingredients’ are ready and your ‘recipe’ is chosen, you can start the cooking process, which in our case is generating text based on user inputs.

Recommended Usage Guidelines

• Commercial Use: If you intend to use this model for commercial purposes, please inform us at info@axcxept.com.
• Attribution: When using or adapting this model, provide attribution as follows: This project utilizes HODACHIBorea-Phi-3.5-mini-Instruct-Common, a model based on Phi-3.5-mini-Instruct and fine-tuned by Axcxept co., ltd.
• Feedback: Reach out with any feedback on your experience. Your insights help us improve!

Troubleshooting

If you encounter any issues while loading the model or generating text, consider the following troubleshooting steps:

• Ensure that the correct version of all necessary packages is installed.
• Check that your device supports CUDA if you’re trying to leverage GPU acceleration.
• Verify that you’re using the correct model identifier.

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

Conclusion

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.