---
hip: 0003
title: Jin Multimodal AI Architecture
author: Hanzo AI Team
type: Standards Track
category: Core
status: Draft
created: 2025-01-09
requires: HIP-1, HIP-2
---


# HIP-0003: 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](https://github.com/hanzoai/jin)

## Specification

### Model Architecture

```python
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

| Model | Parameters | Experts | Context | Modalities | Use Case |
|-------|------------|---------|---------|------------|----------|
| Jin-nano | 1B | 8 | 8K | Text, Vision | Edge devices, mobile |
| Jin-mini | 7B | 16 | 32K | Text, Vision, Audio | Personal assistants |
| Jin-base | 32B | 32 | 64K | All | Standard deployment |
| Jin-large | 175B | 64 | 128K | All + Video | Enterprise AI |
| Jin-ultra | 1T+ | 128 | 256K | All + Specialized | Research, AGI |

### Joint Embedding Space

```yaml
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)

```python
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
```yaml
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
```yaml
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
```yaml
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

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

#### Cloud Deployment (Jin-base)
```yaml
Quantization: FP16/BF16
Memory: 64GB (2x A100)
Latency: <20ms first token
Throughput: >1000 tokens/sec
Scaling: Horizontal via ray.io
```

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

### API Specification

```python
# 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

| Benchmark | Jin-nano | Jin-base | Jin-large | GPT-4V | Gemini Ultra |
|-----------|----------|----------|-----------|--------|--------------|
| MMLU | 72.3 | 86.4 | 91.2 | 86.4 | 90.0 |
| HumanEval | 65.2 | 78.9 | 85.6 | 67.0 | 74.4 |
| VQA v2 | 78.5 | 84.2 | 88.7 | 77.2 | 82.3 |
| AudioCaps | 71.3 | 82.6 | 87.4 | N/A | 79.1 |
| ShapeNet | 68.9 | 79.4 | 84.1 | N/A | N/A |

### Integration with HLLMs

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

```python
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)
```