---
hip: 0002
title: Hamiltonian Large Language Models (HLLMs) Specification
author: Hanzo AI Team
type: Standards Track
category: Core
status: Draft
created: 2024-12-20
updated: 2025-01-09
requires: HIP-1
---


# HIP-0002: Hamiltonian Large Language Models (HLLMs) Specification

## Abstract

This proposal defines the architecture, capabilities, and standards for Hamiltonian Large Language Models (HLLMs). HLLMs are multimodal AI models with per-user fine-tuning, where every user owns their personalized model fork. These models support text, vision, audio, and 3D modalities with unified representations and cross-modal understanding.

## Specification

### Model Architecture

#### Unified Transformer Architecture with Hamiltonian Dynamics
```python
class HLLMArchitecture:
    modalities = ["text", "vision", "audio", "3d"]
    hidden_dim = 4096  # Base model
    num_layers = 48
    num_heads = 64
    context_length = 32768
    
    # Modality-specific encoders
    text_encoder: "Byte-level BPE"
    vision_encoder: "Vision Transformer patches"
    audio_encoder: "Mel-spectrogram transformer"
    3d_encoder: "Point cloud transformer"
    
    # Unified decoder
    decoder: "Autoregressive transformer"
```

### Model Variants

| Model | Parameters | Context | Modalities | Use Case |
|-------|------------|---------|------------|----------|
| HLLM-7B | 7B | 8K | Text, Vision | Edge deployment, personal devices |
| HLLM-32B | 32B | 32K | Text, Vision, Audio | Standard per-user models |
| HLLM-175B | 175B | 128K | All | Advanced personal assistants |
| HLLM-1T | 1T | 256K | All + specialized | Research & collective intelligence |

**Note**: These are BASE models only. Every user interaction creates a personalized fork with user-specific LoRA adapters, making each user's model unique.

### Input/Output Specifications

#### Input Format
```json
{
  "inputs": [
    {
      "type": "text",
      "content": "Describe this image"
    },
    {
      "type": "image",
      "content": "base64_encoded_image",
      "encoding": "jpeg"
    },
    {
      "type": "audio",
      "content": "base64_encoded_audio",
      "encoding": "wav",
      "sample_rate": 16000
    }
  ],
  "parameters": {
    "max_tokens": 2048,
    "temperature": 0.7,
    "modality_weights": {
      "text": 1.0,
      "vision": 1.0,
      "audio": 0.8
    }
  }
}
```

#### Output Format
```json
{
  "outputs": [
    {
      "type": "text",
      "content": "Generated text response"
    },
    {
      "type": "image",
      "content": "base64_encoded_image",
      "encoding": "png"
    }
  ],
  "metadata": {
    "model": "HLLM-32B",
    "tokens_used": 1547,
    "latency_ms": 234,
    "modalities_processed": ["text", "vision"]
  }
}
```

### Capabilities

#### Core Capabilities
1. **Cross-modal Understanding**: Understand relationships between modalities
2. **Any-to-Any Generation**: Generate any modality from any input
3. **Zero-shot Transfer**: Apply learning across modalities
4. **Compositional Reasoning**: Combine modalities for complex reasoning

#### Specific Tasks
- **Vision-Language**: Image captioning, VQA, visual reasoning
- **Audio-Language**: Speech recognition, audio description
- **3D-Language**: 3D scene understanding, spatial reasoning
- **Multimodal Generation**: Create images from text+audio, etc.

### Training Infrastructure

#### Base Model Training
- **Text**: 10T tokens from web, books, code
- **Images**: 5B image-text pairs
- **Audio**: 100K hours of audio with transcripts
- **3D**: 10M 3D scenes with annotations
- **Synthetic**: Generated multimodal data for alignment

#### Per-User Fine-Tuning (Automatic)
- **Data**: User's own interactions (encrypted)
- **Compute**: ~35ms per interaction for gradient update
- **Storage**: ~100MB per user for LoRA adapters
- **Privacy**: All training data stays encrypted with user's key
- **Ledger**: Every training operation recorded on-chain

**Key Difference**: Base models are trained once. Per-user models continuously evolve with every interaction, creating billions of unique models.

### Inference Optimization

#### Techniques
1. **Modality Routing**: Process only relevant modalities
2. **Sparse Attention**: Reduce computation for long contexts
3. **Quantization**: INT8/INT4 for edge deployment
4. **Caching**: KV-cache across modalities
5. **Batching**: Dynamic batching for different modalities

#### Performance Targets
- **Latency**: <100ms for first token (HLLM-32B)
- **Throughput**: >1000 tokens/second (batched)
- **Memory**: <16GB for HLLM-7B inference

### Safety and Alignment

#### Safety Measures
1. **Content Filtering**: Multi-modal content moderation
2. **Watermarking**: Invisible watermarks in generated content
3. **Attribution**: Track training data influence
4. **Bias Mitigation**: Cross-modal debiasing techniques

#### Alignment Techniques
- **RLHF**: Reinforcement Learning from Human Feedback
- **Constitutional AI**: Rule-based constraints
- **Multimodal Alignment**: Cross-modal consistency checks

### Open Source Repositories
- **Jin Multimodal Models**: [github.com/hanzoai/jin](https://github.com/hanzoai/jin)
- **LLM Gateway**: [github.com/hanzoai/llm](https://github.com/hanzoai/llm)
- **Agent Framework**: [github.com/hanzoai/agent](https://github.com/hanzoai/agent)
- **MCP Tools**: [github.com/hanzoai/mcp](https://github.com/hanzoai/mcp)
- **Chat Platform**: [github.com/hanzoai/chat](https://github.com/hanzoai/chat)
- **Search Engine**: [github.com/hanzoai/search](https://github.com/hanzoai/search)

### Model Checkpoints
- **HLLM-7B Base**: [huggingface.co/hanzoai/hllm-7b](https://huggingface.co/hanzoai/hllm-7b)
- **HLLM-32B Base**: [huggingface.co/hanzoai/hllm-32b](https://huggingface.co/hanzoai/hllm-32b)
- **Evaluation Suite**: [github.com/hanzoai/hllm-eval](https://github.com/hanzoai/hllm-eval)

3. [Gemini: A Family of Multimodal Models](https://arxiv.org/abs/2312.11805)
4. [HIP-0: Hanzo AI Architecture](./hip-0.md)
5. [HIP-5: Post-Quantum Security](./hip-5.md)

## Copyright

Copyright and related rights waived via [CC0](https://creativecommons.org/publicdomain/zero/1.0/).