The Phi-3-Vision-128K-Instruct model is a revolutionary tool for understanding visual and textual inputs, designed with vast applications in both commercial and research environments. This guide will walk you through the essentials of using this powerful multimodal model.

Model Overview

The Phi-3-Vision-128K-Instruct is an advanced multimodal model engineered from a variety of high-quality datasets. Utilizing both text and images, it is designed for applications that require extensive reasoning, high memory capacity, and quick response times. Whether you’re conducting research or developing AI-powered features, this model serves as a robust foundation.

Getting Started

Installation Steps

To integrate the Phi-3 model into your projects, follow these steps:

• Ensure you have the transformers library (development version 4.40.2) installed:

pip uninstall -y transformers
pip install git+https://github.com/huggingface/transformers

• For loading the model, add trust_remote_code=True in your from_pretrained() function.

Example Usage

Employ the model effectively by utilizing the chat format for prompts. You can use a single image or engage in multi-turn conversations. For example:

markdown
user
nimage_1
nprompt
endn
assistant
n

Analogy: Understanding the Functionality

Think of the Phi-3 model as a highly skilled bilingual interpreter who can adeptly communicate about complex topics in both text and images. Just as an interpreter listens carefully, thinks deeply, and delivers answers, the Phi-3 model receives images and text, processes them, and provides coherent responses based on its training from an array of data. The more context it receives (much like detailed conversations), the better and more nuanced the interpretation it can provide.

Sample Inference Code

Here’s a quick example to get started with running the model:

python
from PIL import Image
import requests
from transformers import AutoModelForCausalLM, AutoProcessor

model_id = "microsoft/Phi-3-vision-128k-instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto")
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

url = "https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/202404/BMDataViz_661fb89f3845e.png"
image = Image.open(requests.get(url, stream=True).raw)

messages = [
    {"role": "user", "content": "What is shown in this image?"},
    {"role": "assistant", "content": "The chart displays the percentage of respondents who agree with various statements..."},
]
prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")

generation_args = {"max_new_tokens": 500, "temperature": 0.0, "do_sample": False}
generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args)
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(response)

Troubleshooting

Here are some common troubleshooting tips:

• If you encounter memory issues, ensure that you are using a compatible and sufficient GPU.
• If the model fails to load, double-check your installation and make sure you’ve followed the steps correctly.
• For unexpected output, validate the input format and ensure the data aligns with the model’s requirements.
• Refer to the sample inference code for further examples.

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Responsible AI Considerations

As you leverage this model, it’s essential to understand its limitations. Ensure compliance with applicable regulations, and be cautious of the potential for generating unfair, unreliable, or offensive content. Continuous evaluation and mitigation strategies should be employed whenever this model is put to use, especially in sensitive applications.

Conclusion

With its state-of-the-art capabilities and vast potential applications, the Phi-3-Vision-128K-Instruct model is a robust tool for those looking to innovate in the fields of natural language processing and computer vision. By following the guidelines in this article, you will be well-equipped to harness its power.

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.