up

app.py

@@ -9,7 +9,7 @@ def initialize_model():
     model_config = HIMConfig()
     env_config = EnvironmentConfig()
     device = torch.device("cuda" if torch.cuda.is_available() and env_config.device == "cuda" else "cpu")
-    model = HIMModel(model_config.to_dict()).to(device)
+    model = HIMModel(model_config).to(device)  # Pass model_config directly instead of model_config.to_dict()
     return model
 
 async def chat(message: str,

config/__init__.py

@@ -0,0 +1,2 @@
+from .model_config import HIMConfig
+from .environment_config import EnvironmentConfig

config/environment_config.py

@@ -3,21 +3,17 @@ from typing import Optional
 
 @dataclass
 class EnvironmentConfig:
-    # Hugging Face configuration
-    hf_model_path: str = "TeleologyHI/HIM-self"
-    hf_token: Optional[str] = None
+    def __init__(self):
+        self.device = "cuda"  # or "cpu"
+        self.log_level = "INFO"
+        self.api_port = 8000
+        self.threads = 4
 
-    # Hardware configuration
-    device: str = "cuda"
-    num_gpus: int = 1
-    mixed_precision: bool = True
-
-    # Logging configuration
-    log_level: str = "INFO"
-    enable_wandb: bool = False
-    wandb_project: str = "HIM-self"
-
-    # API configuration
-    api_host: str = "0.0.0.0"
-    api_port: int = 7860
-    enable_cors: bool = True
+    def to_dict(self):
+        """Convert config to dictionary format"""
+        return {
+            "device": self.device,
+            "log_level": self.log_level,
+            "api_port": self.api_port,
+            "threads": self.threads
+        }

config/model_config.py

@@ -3,39 +3,23 @@ from typing import Dict, List
 
 @dataclass
 class HIMConfig:
-    # Base model configuration
-    model_name: str = "HIM-self"
-    base_model: str = "gpt2"
-    max_length: int = 512
-    temperature: float = 0.7
-    top_p: float = 0.95
+    def __init__(self):
+        self.base_model = "gpt2-medium"
+        self.max_length = 1024
+        self.temperature = 0.7
+        self.top_p = 0.95
+        self.consciousness_integration_factor = 0.8
+        self.emotional_intelligence_weight = 0.6
+        self.semiotic_processing_depth = 3
 
-    # Consciousness parameters
-    self_awareness_level: float = 0.8
-    ethical_reasoning_weight: float = 0.9
-    symbolic_interpretation_capacity: float = 0.85
-    consciousness_dimension: int = 768
-    attention_heads: int = 12
-
-    # Teleological parameters
-    purpose_driven_bias: float = 0.75
-    spiritual_awareness: float = 0.8
-    meaning_dimension: int = 256
-
-    # Training configuration
-    batch_size: int = 8
-    learning_rate: float = 2e-5
-    num_train_epochs: int = 3
-    gradient_accumulation_steps: int = 1
-    warmup_steps: int = 500
-
-    # Architecture configuration
-    hidden_size: int = 768
-    intermediate_size: int = 3072
-    num_hidden_layers: int = 12
-    num_attention_heads: int = 12
-
-    # Memory configuration
-    memory_size: int = 1024
-    context_length: int = 2048
-    cache_size: int = 512
+    def to_dict(self):
+        """Convert config to dictionary format"""
+        return {
+            "base_model": self.base_model,
+            "max_length": self.max_length,
+            "temperature": self.temperature,
+            "top_p": self.top_p,
+            "consciousness_integration_factor": self.consciousness_integration_factor,
+            "emotional_intelligence_weight": self.emotional_intelligence_weight,
+            "semiotic_processing_depth": self.semiotic_processing_depth
+        }