HIP-2: 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.

Motivation

Current AI models are typically specialized for single modalities or use separate encoders for different modalities. Additionally, they use domain-specific fine-tuning which fundamentally fails users:

Information Loss: Separate models lose cross-modal relationships
Inefficiency: Multiple models increase computational overhead
No Personalization: Domain-specific models (medical, legal, etc.) still generic for individuals
No User Ownership: Users can't own their personalized AI
Privacy Violations: User data pooled for domain training

HLLMs address these limitations by providing unified multimodal understanding with per-user fine-tuning where every interaction creates a personalized model fork, integrated with Active Inference principles for principled decision-making.

Specification

Model Architecture

Unified Transformer Architecture with Hamiltonian Dynamics

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

{
  "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

{
  "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

Cross-modal Understanding: Understand relationships between modalities
Any-to-Any Generation: Generate any modality from any input
Zero-shot Transfer: Apply learning across modalities
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

Modality Routing: Process only relevant modalities
Sparse Attention: Reduce computation for long contexts
Quantization: INT8/INT4 for edge deployment
Caching: KV-cache across modalities
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

Content Filtering: Multi-modal content moderation
Watermarking: Invisible watermarks in generated content
Attribution: Track training data influence
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

Rationale

Why Unified Architecture?

A unified architecture enables:

Shared Representations: Learn common patterns across modalities
Efficient Scaling: Single model scales better than multiple
Emergent Capabilities: Cross-modal understanding emerges naturally

Why These Modalities?

Text, vision, audio, and 3D cover the primary human senses and most digital content:

Text: Foundation of human knowledge
Vision: Rich visual understanding
Audio: Speech and environmental sounds
3D: Spatial reasoning and robotics

Why Multiple Model Sizes?

Different applications have different requirements:

Edge: HLLM-7B for mobile and embedded
Cloud: HLLM-32B for standard applications
Enterprise: HLLM-175B for complex tasks
Research: HLLM-1T for pushing boundaries

Backwards Compatibility

HLLMs maintain compatibility with existing standards:

OpenAI API: Compatible request/response format
Hugging Face: Transformers library support
ONNX: Export for cross-platform deployment
MCP: Model Context Protocol integration

Test Cases

Unit Tests

Modality encoder/decoder functionality
Cross-attention mechanisms
Input/output formatting

Integration Tests

End-to-end multimodal generation
API compatibility tests
Performance benchmarks

Evaluation Benchmarks

MMLU: Multitask language understanding
VQA2.0: Visual question answering
COCO: Image captioning
LibriSpeech: Speech recognition
ScanNet: 3D scene understanding

Research Foundation

Published Papers

Multimodal Transformers: arxiv.org/abs/2024.hanzo.mmt (forthcoming)
Per-User Fine-Tuning at Scale: arxiv.org/abs/2024.hanzo.puft (forthcoming)
Hamiltonian Dynamics in Neural Networks: arxiv.org/abs/2024.hanzo.hdnn (forthcoming)

Open Source Repositories

Jin Multimodal Models: github.com/hanzoai/jin
LLM Gateway: github.com/hanzoai/llm
Agent Framework: github.com/hanzoai/agent
MCP Tools: github.com/hanzoai/mcp
Chat Platform: github.com/hanzoai/chat
Search Engine: github.com/hanzoai/search

Hamiltonian Large Language Models (HLLMs) Specification