HIP-3: Jin Multimodal AI Architecture

Abstract

This proposal defines the Jin architecture, Hanzo's unified multimodal AI framework supporting text, vision, audio, and 3D modalities through joint embedding spaces. Jin represents our next-generation foundational model with variants from nano (1B) to ultra (1T+) parameters, featuring diffusion transformer MoE architectures and cross-modal understanding.

Repository: github.com/hanzoai/jin

Motivation

Current multimodal models suffer from:

1. Modality Silos: Separate encoders lose cross-modal relationships
2. Scale Limitations: Difficulty scaling to trillion+ parameters
3. Training Inefficiency: Redundant learning across modalities
4. Inference Bottlenecks: Sequential processing of modalities
5. Limited 3D Understanding: Poor spatial reasoning capabilities

Jin addresses these through unified joint embedding spaces with efficient MoE routing.

Specification

Model Architecture

class JinArchitecture:
    """
    Jin: Unified multimodal AI with joint embedding spaces
    """
    # Core Configuration
    embedding_dim = 8192  # Unified embedding dimension
    num_experts = 128     # MoE experts
    experts_per_token = 8 # Active experts
    
    # Modality Encoders
    text_encoder = "RoPE Transformer"
    vision_encoder = "DiT (Diffusion Transformer)"
    audio_encoder = "Conformer"
    mesh_encoder = "Point Transformer v3"
    
    # Joint Embedding
    joint_space = "Hyperbolic manifold"
    alignment = "Contrastive + Diffusion"

Model Variants

Joint Embedding Space

Embedding Structure:
  Dimension: 8192
  Geometry: Hyperbolic (Poincaré ball)
  Radius: 1.0
  
Modality Projections:
  Text → Embedding:
    - Byte-level tokenization
    - RoPE position encoding
    - Layer normalization
    
  Vision → Embedding:
    - Patch-based encoding (16x16)
    - 2D RoPE for positions
    - Multi-scale features
    
  Audio → Embedding:
    - Mel-spectrogram input
    - Conformer blocks
    - Temporal pooling
    
  3D → Embedding:
    - Point cloud sampling (2048 points)
    - KNN graph construction
    - Geometric features

Mixture of Experts (MoE)

class JinMoE:
    def forward(self, x, modality):
        # Router network
        router_logits = self.router(x)
        expert_weights = torch.topk(router_logits, k=8)
        
        # Expert execution
        expert_outputs = []
        for idx, weight in expert_weights:
            expert = self.experts[idx]
            output = expert(x, modality)
            expert_outputs.append(weight * output)
        
        # Combine expert outputs
        return sum(expert_outputs)

Training Pipeline

Phase 1: Unimodal Pretraining

Text Pretraining:
  Data: 15T tokens (web, books, code, papers)
  Objective: Next token prediction
  Duration: 4 weeks on 1024 H100s

Vision Pretraining:
  Data: 10B images
  Objective: DINO v3 + MAE
  Duration: 2 weeks on 512 H100s

Audio Pretraining:
  Data: 1M hours
  Objective: Wav2vec 2.0
  Duration: 1 week on 256 H100s

3D Pretraining:
  Data: 100M 3D objects
  Objective: Point-BERT
  Duration: 1 week on 256 H100s

Phase 2: Joint Alignment

Alignment Training:
  Pairs:
    - Text-Image: 5B pairs
    - Text-Audio: 100M pairs
    - Text-3D: 10M pairs
    - Image-Audio: 50M pairs
  
  Objectives:
    - Contrastive learning (CLIP-style)
    - Diffusion alignment
    - Cycle consistency
  
  Duration: 2 weeks on 1024 H100s

Phase 3: Instruction Tuning

Instruction Data:
  - Text instructions: 10M examples
  - Multimodal tasks: 5M examples
  - Tool use: 1M examples
  - Reasoning: 1M examples

Fine-tuning:
  Method: LoRA + QLoRA
  Rank: 256
  Alpha: 512
  Duration: 1 week on 512 H100s

Inference Optimization

class JinInference:
    """
    Optimized inference with caching and batching
    """
    def __init__(self):
        self.kv_cache = {}
        self.expert_cache = {}
        
    def generate(self, inputs, max_tokens=2048):
        # Modality routing
        modalities = self.detect_modalities(inputs)
        
        # Parallel encoding
        embeddings = self.parallel_encode(inputs, modalities)
        
        # Cached decoding
        outputs = []
        for i in range(max_tokens):
            logits = self.decode_step(embeddings, self.kv_cache)
            token = self.sample(logits)
            outputs.append(token)
            
            if token == self.eos_token:
                break
                
        return outputs

Deployment Configurations

Edge Deployment (Jin-nano)

Quantization: INT4
Memory: <4GB
Latency: <50ms first token
Throughput: >100 tokens/sec
Devices: iPhone 15+, Pixel 8+

Cloud Deployment (Jin-base)

Quantization: FP16/BF16
Memory: 64GB (2x A100)
Latency: <20ms first token
Throughput: >1000 tokens/sec
Scaling: Horizontal via ray.io

Enterprise Deployment (Jin-large)

Quantization: FP16
Memory: 350GB (8x A100)
Latency: <30ms first token
Throughput: >500 tokens/sec
Features: Multi-tenancy, audit logs

API Specification

# Python SDK
from hanzoai import Jin

model = Jin("jin-base")

# Text generation
response = model.generate("Explain quantum computing")

# Multimodal generation
response = model.generate([
    {"type": "text", "content": "Describe this image"},
    {"type": "image", "content": image_bytes}
])

# Cross-modal generation
response = model.generate(
    prompt="Generate an image of a sunset",
    modality_out="image"
)

# 3D generation
response = model.generate(
    prompt="Create a 3D model of a chair",
    modality_out="mesh"
)

Performance Benchmarks

Integration with HLLMs

Jin models can be deployed as base models for HLLMs (HIP-2):

class HLLM_Jin(HLLM):
    """
    HLLM with Jin as base model
    """
    def __init__(self):
        self.base_model = Jin("jin-base")
        self.hamiltonian = HamiltonianDynamics()
        self.active_inference = ActiveInference()
        
    def forward(self, x):
        # Jin embedding
        embeddings = self.base_model.encode(x)
        
        # Hamiltonian evolution
        h_state = self.hamiltonian(embeddings)
        
        # Active inference planning
        actions = self.active_inference.plan(h_state)
        
        return self.base_model.decode(actions)

Rationale

Why Joint Embedding Spaces?

Joint embedding enables:

• True multimodal understanding: Shared representations across modalities
• Zero-shot transfer: Apply learning from one modality to another
• Efficient scaling: Single model instead of multiple specialists
• Emergent capabilities: Cross-modal reasoning emerges naturally

Why Hyperbolic Geometry?

Hyperbolic spaces offer:

• Hierarchical representation: Natural for tree-like structures
• Exponential capacity: More representational power
• Better separation: Improved clustering of concepts
• Semantic preservation: Maintains relationships

Why MoE Architecture?

Mixture of Experts provides:

• Conditional computation: Only activate relevant experts
• Efficient scaling: Scale to trillions of parameters
• Specialization: Experts can specialize by modality/task
• Fast inference: Sparse activation reduces compute

Implementation Roadmap

Phase 1: Jin-nano (Q1 2025)

• 1B parameter model
• Text + Vision modalities
• Edge deployment ready
• Open source release

Phase 2: Jin-base (Q2 2025)

• 32B parameter model
• All core modalities
• Cloud deployment
• API access

Phase 3: Jin-large (Q3 2025)

• 175B parameter model
• Video support
• Enterprise features
• Fine-tuning API

Phase 4: Jin-ultra (Q4 2025+)

• 1T+ parameter model
• Specialized modalities
• AGI research
• Academic partnerships

Security Considerations

Model Security

• Watermarking: Invisible watermarks in generated content
• Safety filters: Multimodal content filtering
• Adversarial robustness: Defense against attacks
• Privacy: No training data memorization

Deployment Security

• TEE inference: Secure enclaves for sensitive data
• Encrypted models: Model encryption at rest
• Access control: Fine-grained permissions
• Audit logging: Complete inference trails

References

1. Flamingo: Visual Language Model
2. Gemini: Multimodal Models
3. Switch Transformers: MoE
4. Hyperbolic Neural Networks
5. HIP-2: HLLMs Specification
6. Jin Repository

Copyright

Copyright and related rights waived via CC0.