In the realm of artificial intelligence, leveraging multi-modal models like LeroyDyerMixtral AI Vision-Instruct X allows for the integration of both vision and text functionalities. This guide will walk you through how to effectively employ this powerful model and troubleshoot common issues.

Getting Started

To embark on your journey with this multi-modal model, you need to follow a series of simple steps. Ensure you have the latest version of Koboldcpp installed to access the vision functionality.

Loading the Model

To harness the model’s capabilities, start by obtaining the appropriate mmproj file. You can find it within the model repository:

LeroyDyerMixtral_AI_Vision-Instruct_X

To load the model, select the file according to your requirements:

• For loading 4-bit: mmproj-Mixtral_AI_Vision-Instruct_X-Q4_0
• For loading 8-bit: mmproj-Mixtral_AI_Vision-Instruct_X-Q8_0
• For loading 16-bit: mmproj-Mixtral_AI_Vision-Instruct_X-f16

Utilizing the Model with Python

To implement the model using Python and specifically to integrate image inputs, follow the instructions below:

from transformers import AutoProcessor, LlavaForConditionalGeneration
from transformers import BitsAndBytesConfig
import torch

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)

model_id = 'LeroyDyerMixtral_AI_Vision-Instruct_X'
processor = AutoProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, device_map='auto')

import requests
from PIL import Image

image1 = Image.open(requests.get('https://llava-vl.github.io/static/images/view.jpg', stream=True).raw)
image2 = Image.open(requests.get('http://images.cocodataset.org/val2017/000000000039.jpg', stream=True).raw)

display(image1)
display(image2)

Think of the code above like setting up a recipe. First, you assemble your ingredients (importing necessary libraries), then you follow steps to achieve the desired dish (loading images and models). Each function plays a distinct role, just like each ingredient contributes to the overall flavor.

Making Predictions

Now you can generate prompts and make predictions based on the images. Here’s how you can format your inputs:

prompts = [
    "USER: imagen What are the things I should be cautious about when I visit this place? What should I bring with me?",
    "USER: imagen Please describe this imagenAssistant:",
]

inputs = processor(prompts, images=[image1, image2], padding=True, return_tensors='pt').to('cuda')

for k, v in inputs.items():
    print(k, v.shape)

Troubleshooting

If you encounter issues at any stage, consider the following troubleshooting tips:

• Ensure you have the latest version of Koboldcpp installed as outdated versions may not support the functionalities you are trying to use.
• Double-check the URLs you used for loading images. They should be accessible and correct.
• Verify that you have installed all required libraries and dependencies in your Python environment.

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Summarizing the Instructions

In summary, the integration of multimodal capabilities can dramatically enhance the interaction with AI models. Following the outlined steps will help you load and utilize the LeroyDyerMixtral AI Vision-Instruct X model effectively.

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.