ollama - 💡(How to fix) Fix Non-Transformer AI Architecture — Instant Response, Persistent Memory, Ollama Compatible [1 participants]

import torch
import numpy as np

# Define the AI-CORE system interface
class AI_Core_Interface:
    def __init__(self, model):
        self.model = model

    def generate_response(self, input_data):
        # Perform GPU-native color-binary coordinate space evaluation
        response = self.model.evaluate(input_data)
        return response

# Define the Ollama model interface
class Ollama_Model_Interface:
    def __init__(self, model):
        self.model = model

    def generate_response(self, input_data):
        # Perform token-by-token generation
        response = self.model.generate(input_data)
        return response

# Hybrid approach: use AI-CORE for instant response and Ollama for deep language tasks
class Hybrid_Model:
    def __init__(self, ai_core_model, ollama_model):
        self.ai_core_model = ai_core_model
        self.ollama_model = ollama_model

    def generate_response(self, input_data):
        # Use AI-CORE for instant response
        instant_response = self.ai_core_model.generate_response(input_data)
        # Use Ollama for deep language tasks
        deep_response = self.ollama_model.generate_response(input_data)
        return instant_response, deep_response

# Initialize the hybrid model
ai_core_model = AI_Core_Interface(torch.nn.Module())
ollama_model = Ollama_Model_Interface(torch.nn.Module())
hybrid_model = Hybrid_Model(ai_core_model, ollama_model)

# Test the hybrid model
input_data = np.random.rand(1, 10)
instant_response, deep_response = hybrid_model.generate_response(input_data)
print(instant_response, deep_response)