Welcome to the realm of OmniFusion, an advanced multimodal AI model designed to extend the capabilities of traditional language processing systems! In this guide, we’ll cover how to set up and use OmniFusion for your projects, diving into the integrations of images and potentially audio, 3D, and video items.

What is OmniFusion?

OmniFusion takes AI to the next level by integrating multiple data modalities into its processing, enabling machines to interpret not just text, but also visual data seamlessly. This means that your AI model can understand images and respond in a highly intelligent manner!

Setting Up OmniFusion

• Step 1: Install necessary libraries
• Step 2: Download the OmniFusion models from Hugging Face
• Step 3: Load the models and the required libraries as follows:

python
import torch
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM
from urllib.request import urlopen
from huggingface_hub import hf_hub_download

# Loading some sources of the projection adapter and image encoder
hf_hub_download(repo_id='AIRI-Institute/OmniFusion', filename='models.py', local_dir='.')
from models import CLIPVisionTower

DEVICE = 'cuda:0'
PROMPT = 'This is a dialog with AI assistant.'
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/OmniFusion', subfolder='OmniMistral-v1_1tokenizer', use_fast=False)
model = AutoModelForCausalLM.from_pretrained('AIRI-Institute/OmniFusion', subfolder='OmniMistral-v1_1tuned-model',
                                             torch_dtype=torch.bfloat16, device_map=DEVICE)

Understanding the Code: An Analogy

Think of setting up OmniFusion as preparing a multi-course meal. Each step in the code represents a task in preparing the meal:

• Gathering Ingredients: Importing libraries loads the necessary ingredients for your model.
• Preparing Dishes: Downloading models is akin to preparing your main dishes (the AI model) that will serve your guests (users).
• Cooking: Loading the model prepares it to serve or respond to prompts, similar to cooking your meal until it’s ready to be plated and enjoyed.

Generating Answers

Now, let’s see how we can generate answers using OmniFusion:

def gen_answer(model, tokenizer, clip, projection, query, special_embs, image=None):
    bad_words_ids = tokenizer(['n', 's', ':'], add_special_tokens=False).input_ids + [[13]]
    gen_params = {
        'do_sample': False,
        'max_new_tokens': 50,
        'early_stopping': True,
        'num_beams': 3,
        'repetition_penalty': 1.0,
        'remove_invalid_values': True,
        'eos_token_id': 2,
        'pad_token_id': 2,
        'forced_eos_token_id': 2,
        'use_cache': True,
        'no_repeat_ngram_size': 4,
        'bad_words_ids': bad_words_ids,
        'num_return_sequences': 1
    }
    
    with torch.no_grad():
        image_features = clip.image_processor(image, return_tensors='pt')
        image_embedding = clip(image_features['pixel_values']).to(device=DEVICE, dtype=torch.bfloat16)
        projected_vision_embeddings = projection(image_embedding).to(device=DEVICE, dtype=torch.bfloat16)
        
        prompt_ids = tokenizer.encode(f'{PROMPT}', add_special_tokens=False, return_tensors='pt').to(device=DEVICE)
        question_ids = tokenizer.encode(query, add_special_tokens=False, return_tensors='pt').to(device=DEVICE)
        
        prompt_embeddings = model.model.embed_tokens(prompt_ids).to(torch.bfloat16)
        question_embeddings = model.model.embed_tokens(question_ids).to(torch.bfloat16)
        
        embeddings = torch.cat([
            prompt_embeddings,
            special_embs['SOI'][None, None, ...],
            projected_vision_embeddings,
            special_embs['EOI'][None, None, ...],
            special_embs['USER'][None, None, ...],
            question_embeddings,
            special_embs['BOT'][None, None, ...]
        ], dim=1).to(dtype=torch.bfloat16, device=DEVICE)
        
        out = model.generate(inputs_embeds=embeddings, **gen_params)
        
    out = out[:, 1:]  
    generated_texts = tokenizer.batch_decode(out)[0]
    return generated_texts

Common Troubleshooting Steps

If you encounter any issues while implementing OmniFusion, consider the following troubleshooting ideas:

• Ensure all libraries are installed correctly.
• Check if the model paths are correctly provided.
• Validate that your device supports the required CUDA configuration.
• If you receive errors regarding missing files or directories, recheck the filenames and their locations.

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

Conclusion and Future Plans

OmniFusion is paving the way for more sophisticated AI interactions! With plans to integrate even more modalities, such as sound, 3D, and video, the future looks promising. Stay tuned for updates on GitHub!

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.