HIP-6: Per-User Fine-Tuning Architecture for Personalized AI

Abstract

This proposal defines Hanzo's per-user fine-tuning architecture where every user interaction automatically creates and updates a personalized model fork. Each user owns their unique AI model that learns from their interactions, with all training data and model evolution stored on an immutable ledger. This eliminates domain-specific pre-training in favor of continuous per-user adaptation.

Motivation

Current AI systems use domain-specific fine-tuning (e.g., "medical AI", "legal AI") which has fundamental limitations:

1. One-Size-Fits-None: Domain models still too generic for individuals
2. Privacy Violation: User data pooled for domain training
3. No Ownership: Users don't own their personalization
4. Slow Adaptation: Batch training instead of real-time learning
5. Lost Context: User history not preserved across sessions

Per-user fine-tuning solves these by giving each user their own evolving model.

Specification

Per-User Model Lifecycle

class UserModelLifecycle:
    def __init__(self, user_id: str, base_model: str = "HMM-32B"):
        # Every user starts with base model fork
        self.model = fork_base_model(base_model, user_id)
        self.training_history = []
        self.priors = PersonalPriors()
        
    def interact(self, input: UserInput) -> ModelOutput:
        # 1. Generate response
        output = self.model.generate(input)
        
        # 2. Create training record
        training_op = TrainingOp(
            input=input,
            output=output,
            timestamp=now(),
            gradient=compute_gradient(input, output)
        )
        
        # 3. Update model (real-time)
        self.model.apply_gradient(training_op.gradient)
        
        # 4. Record on ledger
        ledger.record(self.user_id, training_op)
        
        # 5. Update priors
        self.priors.update(training_op)
        
        return output

Model Forking Architecture

pub struct ModelFork {
    // Unique per user
    pub fork_id: ForkId,
    pub user_id: UserId,
    
    // Base model reference (not copied)
    pub base_model: ModelReference,
    pub fork_height: u64,
    
    // User-specific layers (LoRA-style)
    pub user_layers: UserAdapterLayers,
    
    // Personalization state
    pub personalization: PersonalizationState,
}

pub struct UserAdapterLayers {
    // Low-rank adaptation layers
    pub attention_adapters: Vec<LoRAAdapter>,
    pub ffn_adapters: Vec<LoRAAdapter>,
    
    // User-specific embeddings
    pub user_embeddings: PersonalEmbeddings,
    
    // Learned preferences
    pub preference_weights: PreferenceMatrix,
}

Real-Time Fine-Tuning

class RealtimeFineTuning:
    def apply_gradient(self, gradient: Gradient):
        # Immediate model update (no batching)
        with torch.no_grad():
            # Update LoRA adapters only (efficient)
            for adapter in self.user_layers:
                adapter.weight += self.lr * gradient[adapter.name]
            
            # Update user embeddings
            self.user_embeddings.update(gradient.embeddings)
            
            # Adjust preference matrix
            self.preference_weights *= (1 + gradient.preferences)
    
    def compute_gradient(self, input: Tensor, output: Tensor) -> Gradient:
        # Compute gradient from single interaction
        loss = self.compute_loss(input, output)
        gradient = torch.autograd.grad(loss, self.user_layers.parameters())
        
        # Apply privacy noise (differential privacy)
        gradient = add_dp_noise(gradient, epsilon=1.0)
        
        return gradient

Training Record Structure

message UserTrainingRecord {
    // User and model identifiers
    string user_id = 1;
    string fork_id = 2;
    uint64 interaction_number = 3;
    
    // Interaction data (encrypted)
    bytes encrypted_input = 4;
    bytes encrypted_output = 5;
    
    // Model update
    bytes encrypted_gradient = 6;
    float learning_rate = 7;
    
    // Feedback and metrics
    UserFeedback feedback = 8;
    PerformanceMetrics metrics = 9;
    
    // Priors update
    PriorDelta prior_update = 10;
    
    // Cryptographic proof
    bytes zkp_proof = 11;
    bytes signature = 12;
}

Privacy-Preserving Implementation

class PrivateUserModel:
    def __init__(self, user_id: str):
        # All user data encrypted with user's key
        self.encryption_key = derive_key(user_id)
        self.model = EncryptedModel(base_model, self.encryption_key)
        
    def private_inference(self, input: str) -> str:
        # Inference in encrypted space (FHE)
        encrypted_input = encrypt(input, self.encryption_key)
        encrypted_output = self.model.forward_encrypted(encrypted_input)
        output = decrypt(encrypted_output, self.encryption_key)
        return output
    
    def secure_training(self, interaction: Interaction):
        # Training without decryption
        encrypted_gradient = compute_encrypted_gradient(interaction)
        self.model.apply_encrypted_update(encrypted_gradient)
        
        # Zero-knowledge proof of training
        proof = generate_training_proof(interaction, encrypted_gradient)
        ledger.record_with_proof(self.user_id, proof)

Model Evolution and Priors

class ModelEvolution:
    def __init__(self):
        self.generations = []
        self.current_generation = 0
        
    def advance_generation(self, user_models: List[UserModel]):
        # Aggregate learnings from all users (privacy-preserving)
        aggregated_priors = self.aggregate_priors(user_models)
        
        # Create next generation base model
        next_gen_model = self.evolve_base_model(
            current_model=self.base_model,
            priors=aggregated_priors,
            fitness_scores=self.compute_fitness(user_models)
        )
        
        # Record generation advancement
        self.generations.append(Generation(
            number=self.current_generation + 1,
            model=next_gen_model,
            priors=aggregated_priors,
            timestamp=now()
        ))
        
        self.current_generation += 1
    
    def aggregate_priors(self, user_models: List[UserModel]) -> Priors:
        # Federated aggregation without seeing user data
        encrypted_priors = [m.get_encrypted_priors() for m in user_models]
        aggregated = homomorphic_sum(encrypted_priors)
        return aggregated

API Specification

interface UserModelAPI {
  // Create user's personal model
  async createUserModel(userId: string): Promise<ModelId>;
  
  // Interact with personal model
  async chat(
    userId: string,
    message: string,
    options?: ChatOptions
  ): Promise<Response>;
  
  // Get training history
  async getTrainingHistory(
    userId: string,
    auth: AuthProof
  ): Promise<TrainingRecord[]>;
  
  // Export user's model
  async exportModel(
    userId: string,
    format: ExportFormat
  ): Promise<EncryptedModel>;
  
  // Import model from another platform
  async importModel(
    userId: string,
    model: EncryptedModel,
    proof: OwnershipProof
  ): Promise<ModelId>;
}

Storage and Computation

# Per-user storage requirements
user_model:
  base_reference: 8 bytes      # Pointer to base model
  lora_adapters: 100 MB        # User-specific layers
  embeddings: 10 MB            # Personal embeddings
  training_history: 1 MB/month # Compressed history
  total: ~111 MB + growth

# Computation requirements
inference:
  base_model: Shared across users (cached)
  user_adaptation: +5% overhead
  latency: <100ms additional

training:
  gradient_computation: 10ms per interaction
  model_update: 5ms (LoRA only)
  ledger_recording: 20ms
  total: ~35ms per interaction

Rationale

Why Per-User Instead of Domain-Specific?

• True Personalization: Each user is unique, not just their domain
• Privacy: User data never mixed with others
• Ownership: Users own their specific model
• Real-time Learning: Immediate adaptation to user needs
• Portability: Users can move their models between platforms

Why Immediate Fine-Tuning?

• Better UX: Model improves during conversation
• Context Preservation: Never loses conversation context
• Faster Adaptation: Learns user preferences quickly
• Reduced Latency: No batch training delays

Why Immutable Ledger?

• Audit Trail: Complete training history
• Attribution: Credit for data contribution
• Evolution Tracking: See how models improve
• Regulatory Compliance: Provable training data

Implementation Phases

Phase 1: Basic Per-User Models (Q1 2025)

• Simple LoRA adapters per user
• Basic training recording
• Local storage

Phase 2: Real-Time Learning (Q2 2025)

• Live gradient updates
• Conversation context preservation
• Performance optimization

Phase 3: Privacy Features (Q3 2025)

• Encrypted models
• Zero-knowledge proofs
• Federated aggregation

Phase 4: Evolution System (Q4 2025)

• Model generations
• Prior accumulation
• Cross-platform portability

Security Considerations

Privacy

• All user data encrypted with user keys
• Zero-knowledge proofs for training
• No central access to user models

Security

• Post-quantum encryption (inherited from LP-100)
• Secure enclaves for sensitive operations
• Tamper-proof training records

References

1. HIP-1: Hanzo Multimodal Models
2. LP-102: Immutable Training Ledger
3. LoRA: Low-Rank Adaptation
4. Federated Learning

Copyright

Copyright and related rights waived via CC0.