Welcome to the multi-modal world of AI! In this guide, we will explore how to leverage the LeroyDyerMixtral_AI_Vision-Instruct_X model for image-related processing using Python and the Transformers library.

Getting Started with Multi-Modal Capabilities

Before you dive in, ensure you have the most recent versions of Koboldcpp installed. To employ the model’s vision functionality, you’ll need to load the specific **mmproj** file found in the model repository.

Loading the mmproj File

• For 4-bit loading: Use the mmproj-Mixtral_AI_Vision-Instruct_X-Q4_0 file.
• For 8-bit loading: Use the mmproj-Mixtral_AI_Vision-Instruct_X-Q8_0 file.
• For 8-bit float loading: Use the mmproj-Mixtral_AI_Vision-Instruct_X-f16 file.

Using the Transformers Library

1. Setting Up the Model

First, we need to import the necessary classes and set up our model with quantization:

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)

2. Loading Images

Next, let’s load some images that the model will process:

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)

3. Creating Prompts and Processing Inputs

Now, we can generate prompts and prepare the inputs for the model:

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

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

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

Understanding Multi-Modal Functionality through Analogy

Think of the multi-modal capabilities as a chef preparing a gourmet dish. The images can be seen as the raw ingredients, and the model acts as the chef who transforms those ingredients into a delicious meal. Just as a chef needs specific tools and recipes to create a dish, our model requires the correct loading files and prompts to properly process the input images. With the right ‘ingredients,’ you can watch the magic happen as the AI provides valuable insights based on the visuals presented!

Troubleshooting Common Issues

If you encounter any issues while using the LeroyDyerMixtral model, here are some troubleshooting ideas:

• Ensure that you are using the latest version of the Koboldcpp library.
• Check that the mmproj files are properly loaded.
• Verify your image URLs to ensure that they are accessible.
• Confirm that your prompts are correctly formatted and match the expectations of the model.

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

Exploring Further with Pipelines

You can also utilize the pipeline function from the Transformers library:

from transformers import pipeline
from PIL import Image
import requests

model_id = "LeroyDyerMixtral_AI_Vision-Instruct_X"
pipe = pipeline("image-to-text", model=model_id)

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(url, stream=True).raw)

question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
prompt = f"A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. ###Human: imagenquestion###Assistant:"

outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)

Chat Templating and Text-To-Text Interactions

For chat interactions, ensure your prompts are formatted according to the templating instruction format:

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("LeroyDyerMixtral_AI_Vision-Instruct_X")
chat = [
    {"role": "user", "content": "Hello, how are you?"},
    {"role": "assistant", "content": "I’m doing great. How can I help you today?"},
    {"role": "user", "content": "I’d like to show off how chat templating works!"}
]

tokenizer.apply_chat_template(chat, tokenize=False)

Wrapping Up

With this guide, you are now equipped to embrace the extensive capabilities of the LeroyDyerMixtral_AI_Vision-Instruct_X model and explore a variety of applications, from image analysis to interactive chat interfaces.

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.