Welcome to our guide on the incredible Falcon2-11B-VLM model, a state-of-the-art visual language model that combines the power of language understanding and vision capabilities. Whether you’re diving into inference or finetuning, this user-friendly article will walk you through the essentials of implementing this innovative technology.

What is Falcon2-11B-VLM?

The Falcon2-11B-vlm model is a causal decoder-only model boasting 11 billion parameters, developed by TII. Trained on a staggering 5,000 billion tokens, it utilizes data from the RefinedWeb dataset, enhanced further with curated corpora. It merges the pretrained CLIP ViT-L/14 vision encoder with a chat-finetuned model to work effectively with image-text data.

Getting Started: Step-by-Step Guide

To use the Falcon2-11B-VLM model effectively, follow these quick steps:

1. Install Required Libraries: Ensure you have the necessary libraries installed, including PyTorch 2.0 and Transformers.
2. Import Libraries: Use the following code to import required libraries:

from transformers import LlavaNextForConditionalGeneration, LlavaNextProcessor
from PIL import Image
import requests
import torch

3. Load the Model: Instantiate the processor and model from pretrained configurations:

processor = LlavaNextProcessor.from_pretrained("tiiuae/falcon-11B-vlm", tokenizer_class='PreTrainedTokenizerFast')
model = LlavaNextForConditionalGeneration.from_pretrained("tiiuae/falcon-11B-vlm", torch_dtype=torch.bfloat16)

4. Prepare Your Image: Upload the image you want to analyze:

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
cats_image = Image.open(requests.get(url, stream=True).raw)

5. Generate Your Instruction: Fill in the required instruction for the model:

instruction = 'Write a long paragraph about this picture.'
prompt = f"""User:\n{instruction} Falcon:"""

6. Run the Model: Input your data and execute the model:

inputs = processor(prompt, images=cats_image, return_tensors="pt", padding=True).to('cuda:0')
model.to('cuda:0')
output = model.generate(**inputs, max_new_tokens=256)
generated_captions = processor.decode(output[0], skip_special_tokens=True).strip()

7. View Results: Print the generated output:

print(generated_captions)

Understanding the Code Through an Analogy

Think of the Falcon2-11B-VLM model as a talented chef in a bustling kitchen. The ingredients we provide (images and text inputs) are like the raw materials for a delicious dish that the chef will prepare. Here’s how each part of our code works in this analogy:

• Installing Libraries: This is akin to gathering your kitchen tools and utensils. Just like you can’t cook without knives and pots, you can’t run this model without the right libraries.
• Loading the Model: Imagine setting up your workspace with necessary appliances. When you load the processor and model, you’re prepping the chef’s workstation to create your culinary masterpiece.
• Preparing Your Image: Selecting ingredients to cook. You’re choosing the right image, as a chef would select fresh produce for a vibrant dish.
• Generating Instructions: Creating a recipe. You’re providing the steps (instructions) for what the chef should do with the ingredients, which is essential for the final output.
• Running the Model: Cooking! This is where the chef works their magic, mixing all the inputs and producing the final dish (the output captions).
• Viewing Results: Tasting your dish. At this final step, you see the results of your hard work—similar to being served a delish end platter!

Troubleshooting Common Issues

If you encounter any issues while working with the Falcon2-11B-VLM model, consider these troubleshooting suggestions:

• Ensure PyTorch 2.0 is installed: Falcon VLMs require PyTorch 2.0, so double-check your installation.
• Check for CUDA devices: Ensure your GPU is set up correctly and that the model is directed to the appropriate device.
• Image Format Troubles: If your image doesn’t load, try converting it to a standard format like JPEG or PNG.

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Conclusion

By harnessing the capabilities of Falcon2-11B-VLM, developers and researchers can push the envelope of AI research and visual language tasks. 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.