hip-0002

HIP-2: Hamiltonian Large Language Models (HLLMs) Specification. Status Draft. Hanzo's own standard — read this before implementing against it.

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

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

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

Model Checkpoints

HLLM-7B Base: huggingface.co/hanzoai/hllm-7b
HLLM-32B Base: huggingface.co/hanzoai/hllm-32b
Evaluation Suite: github.com/hanzoai/hllm-eval