hips/HIPs/hip-0002-hamiltonian-large-language-models-hllms-specification.md

hip, title, author, type, category, status, created, updated, requires

hip	title	author	type	category	status	created	updated	requires
0002	Hamiltonian Large Language Models (HLLMs) Specification	Hanzo AI Team	Standards Track	Core	Final	2024-12-20	2025-01-09	HIP-0001

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

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

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 — archived 2026-05-12; the multimodal framework was not built (HIP-0003)
• 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

Model Checkpoints

• HLLM-7B Base: huggingface.co/hanzoai/hllm-7b
• HLLM-32B Base: huggingface.co/hanzoai/hllm-32b
• Evaluation Suite: proposed by this HIP; not yet written, so there is nothing to link to.

3. Gemini: A Family of Multimodal Models
4. HIP-0000: Hanzo AI Architecture & Framework
5. HIP-0005: Post-Quantum Security for AI Infrastructure

Copyright

Copyright and related rights waived via CC0.