In this guide, we will navigate through the process of using the multi-modal capabilities of the LeroyDyerMixtral_AI_Vision-Instruct_X model. This model allows you to work with both images and text seamlessly, broadening your AI development horizon significantly. Let’s dive in!

Setting Up the Environment

To get started, ensure that you have the latest version of Koboldcpp installed, as it is essential for accessing the model’s vision functionalities.

Loading the Model

To utilize the model effectively, you need to load the specified mmproj file from the model’s repository. There are different quantization options for loading, depending on your requirements:

• For 4-bit: Use the mmproj file: mmproj-Mixtral_AI_Vision-Instruct_X-Q4_0
• For 8-bit: Use the mmproj file: mmproj-Mixtral_AI_Vision-Instruct_X-Q8_0
• For floating-point 16-bit: Use the mmproj file: mmproj-Mixtral_AI_Vision-Instruct_X-f16

Writing the Code

The below code snippet demonstrates how to import the necessary libraries, configure the model, and load images for processing.

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)

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

inputs = processor(prompts, images=[image1, image2], padding=True, return_tensors="pt").to("cuda")
for k, v in inputs.items():
    print(k, v.shape)

Think of using this multi-modal model like a Swiss Army knife. Each tool within it serves a specific purpose, allowing you to cut, screw, or even open a bottle, depending on your needs for the moment. Similarly, this model provides diverse capabilities that can be tailored to various tasks, whether you want to analyze images or generate text based on them.

Using the Transformer Pipeline

The transformer pipeline can also be utilized for image-to-text tasks. Here’s how you can accomplish this:

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 = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."

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

Instruction Templating with Mistral Chat

If you’re looking to implement a chat-like experience with your AI, here’s how to structure your prompts using instruction templating:

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)

Common Issues & Troubleshooting

If you encounter any issues while using the LeroyDyerMixtral_AI_Vision-Instruct_X model, consider the following troubleshooting tips:

• Ensure libraries are updated: Sometimes, issues arise from outdated libraries. Make sure Koboldcpp and transformers are up to date.
• Check Model Loading: Make sure that you’re loading the correct mmproj file for your intended usage (4-bit or 8-bit).
• Image Accessibility: Verify that the URLs you’re using for images are accessible and correct.
• Device Compatibility: Ensure that your running environment supports CUDA if you plan to leverage GPU acceleration.

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

Conclusion

By utilizing the capabilities of the LeroyDyerMixtral_AI_Vision-Instruct_X, you’re equipped to tackle complex tasks that involve both images and text. As AI continues to evolve, the importance of such multi-modal models cannot be overstated.

Continuous Improvement

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.