AnyModal: A Flexible Multimodal Language Model Framework

AnyModal is a powerful and extensible framework designed for integrating various input modalities—including images and audio—into large language models (LLMs). This innovative solution streamlines the processes of tokenization, encoding, and language generation, leveraging pre-trained models across different modalities.

Key Features

• Flexible Integration: Effortlessly incorporate various input modalities such as vision, audio, and structured data into your applications.
• Tokenization Support: Tokenizes compelling inputs from non-text modalities, seamlessly aligning them with LLMs for enhanced language generation.
• Extensible Design: Customize and enhance the framework by adding new input processors and tokenizers with minimal effort.

Getting Started

To harness the potential of AnyModal, you'll first want to dive into its core components. Below, you’ll find an overview of how to implement input modality tokenization and leverage AnyModal’s capabilities in your projects.

Example: Integrating Vision Modality Using Vision Transformer

This snippet illustrates how to process and integrate image data into your model:

from transformers import ViTImageProcessor, ViTForImageClassification
from anymodal import MultiModalModel
from vision import VisionEncoder, Projector

# Load vision processor and model
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
vision_model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
hidden_size = vision_model.config.hidden_size

# Initialize vision encoder and projector
vision_encoder = VisionEncoder(vision_model)
vision_tokenizer = Projector(in_features=hidden_size, out_features=768)

# Load LLM components
from transformers import AutoTokenizer, AutoModelForCausalLM
llm_tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
llm_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")

# Initialize AnyModal
multimodal_model = MultiModalModel(
    input_processor=None,
    input_encoder=vision_encoder,
    input_tokenizer=vision_tokenizer,
    language_tokenizer=llm_tokenizer,
    language_model=llm_model,
    input_start_token='<|imstart|>',
    input_end_token='<|imend|>',
    prompt_text="The interpretation of the given image is: "
)

Training and Inference

Train and generate predictions seamlessly with AnyModal:

# Training
for epoch in range(num_epochs):
    for batch in train_loader:
        optimizer.zero_grad()
        logits, loss = multimodal_model(batch)
        loss.backward()
        optimizer.step()

# Inference
sample_input = val_dataset[0]['input']
generated_text = multimodal_model.generate(sample_input, max_new_tokens=30)
print(generated_text)

Extending AnyModal

You can expand AnyModal's capabilities further by implementing new input processors and tokenizers. Here’s an example of creating an audio processor:

class AudioProcessor:
    def __init__(self, sample_rate):
        self.sample_rate = sample_rate

    def process(self, audio_data):
        # Your audio preprocessing logic
        pass

Community Contributions

Contributions are encouraged! Whether it’s bug fixing, enhancing documentation, or adding support for novel input modalities, your input is invaluable.

1. Fork the repository and clone it to your local machine.
2. Create a new branch for your feature or improvement.
3. Submit a pull request detailing your changes.

Join our community on r/AnyModal to discuss ideas, ask questions, and showcase your projects built with AnyModal.

**Happy building with AnyModal!