LLM Multi-Modal: Vision-Language, Audio, and Multimodal Systems

LLM Multi-Modal: Vision-Language, Audio, and Multimodal Systems

Multimodal LLMs process and generate across multiple data types including text, images, audio, and video. Production deployment requires specialized encoders, fusion strategies, and cross-modal alignment techniques.

Multimodal Architecture

Multimodal Processing

1. Vision-Language Pipeline

from dataclasses import dataclass
from typing import List, Optional, Dict, Any
from enum import Enum

class Modality(Enum):
    TEXT = "text"
    IMAGE = "image"
    AUDIO = "audio"
    VIDEO = "video"

@dataclass
class MultimodalInput:
    modality: Modality
    data: Any
    metadata: Dict[str, Any]

class VisionLanguageModel:
    def __init__(self):
        self.image_encoder = "clip-vit-large"
        self.text_encoder = "llama-tokenizer"
        self.projection_dim = 1024
        self.max_image_tokens = 576

    def encode_image(self, image_path: str) -> Dict:
        return {
            "embeddings": [0.1] * self.projection_dim,
            "num_patches": 256,
            "spatial_tokens": 576
        }

    def encode_text(self, text: str) -> Dict:
        tokens = text.split()
        return {
            "token_ids": list(range(len(tokens))),
            "num_tokens": len(tokens)
        }

    def project_to_llm_space(self, image_embeddings: List[float],
                              text_embeddings: List[float]) -> List[float]:
        return [a + b for a, b in zip(
            image_embeddings[:self.projection_dim],
            text_embeddings[:self.projection_dim]
        )]

    def generate_caption(self, image_path: str, max_tokens: int = 100) -> str:
        img_features = self.encode_image(image_path)
        return f"Caption for image with {img_features['num_patches']} patches"

    def visual_qa(self, image_path: str, question: str) -> str:
        img_features = self.encode_image(image_path)
        text_features = self.encode_text(question)
        fused = self.project_to_llm_space(img_features["embeddings"],
                                          text_features["token_ids"][:self.projection_dim])
        return f"Answer to: {question[:50]}..."

    def compute_image_token_budget(self, image_size: tuple,
                                    patch_size: int = 14) -> int:
        h, w = image_size
        patches_h = h // patch_size
        patches_w = w // patch_size
        return patches_h * patches_w

2. Audio-Language Pipeline

@dataclass
class AudioInput:
    sample_rate: int
    duration_seconds: float
    channels: int

class AudioLanguageModel:
    def __init__(self):
        self.sample_rate = 16000
        self.chunk_length = 30
        self.embedding_dim = 1280

    def encode_audio(self, audio_path: str) -> Dict:
        return {
            "embeddings": [0.1] * self.embedding_dim,
            "num_frames": 3000,
            "duration_ms": 5000
        }

    def transcribe(self, audio_path: str) -> str:
        features = self.encode_audio(audio_path)
        return f"Transcription of audio ({features['duration_ms']}ms)"

    def audio_qa(self, audio_path: str, question: str) -> str:
        audio_features = self.encode_audio(audio_path)
        return f"Answer about audio: {question[:30]}..."

    def speech_to_speech(self, audio_path: str, instruction: str) -> str:
        return f"Transformed audio based on: {instruction[:30]}..."

Key Formulas

Modality Comparison

Best Practices

1. Align modalities using contrastive learning before fusion
2. Use modality-specific tokenizers to preserve information
3. Implement early fusion for tasks requiring deep cross-modal understanding
4. Cache encoder outputs when processing the same input multiple times
5. Monitor token budgets since images consume many more tokens than text

Production Considerations

Resource Requirements by Modality

Latency Breakdown