fix model names

common/config_models.py

@@ -4,13 +4,13 @@ from typing import List, Optional, Union
 from common.utils import unwrap
 
 
-class config_config_model(BaseModel):
+class ConfigConfig(BaseModel):
     config: Optional[str] = Field(
         None, description=("Path to an overriding config.yml file")
     )
 
 
-class network_config_model(BaseModel):
+class NetworkConfig(BaseModel):
     host: Optional[str] = Field("127.0.0.1", description=("The IP to host on"))
     port: Optional[int] = Field(5000, description=("The port to host on"))
     disable_auth: Optional[bool] = Field(
@@ -28,7 +28,7 @@ class network_config_model(BaseModel):
     )
 
 
-class logging_config_model(BaseModel):
+class LoggingConfig(BaseModel):
     log_prompt: Optional[bool] = Field(False, description=("Enable prompt logging"))
     log_generation_params: Optional[bool] = Field(
         False, description=("Enable generation parameter logging")
@@ -36,7 +36,7 @@ class logging_config_model(BaseModel):
     log_requests: Optional[bool] = Field(False, description=("Enable request logging"))
 
 
-class model_config_model(BaseModel):
+class ModelConfig(BaseModel):
     model_dir: str = Field(
         "models",
         description=(
@@ -171,8 +171,10 @@ class model_config_model(BaseModel):
         ),
     )
 
+    model_config = ConfigDict(protected_namespaces=())
 
-class draft_model_config_model(BaseModel):
+
+class DraftModelConfig(BaseModel):
     draft_model_dir: Optional[str] = Field(
         "models",
         description=(
@@ -209,18 +211,18 @@ class draft_model_config_model(BaseModel):
     )
 
 
-class lora_instance_model(BaseModel):
+class LoraInstanceModel(BaseModel):
     name: str = Field(..., description=("Name of the LoRA model"))
     scaling: float = Field(
         1.0, description=("Scaling factor for the LoRA model (default: 1.0)")
     )
 
 
-class lora_config_model(BaseModel):
+class LoraConfig(BaseModel):
     lora_dir: Optional[str] = Field(
         "loras", description=("Directory to look for LoRAs (default: 'loras')")
     )
-    loras: Optional[List[lora_instance_model]] = Field(
+    loras: Optional[List[LoraInstanceModel]] = Field(
         None,
         description=(
             "List of LoRAs to load and associated scaling factors (default scaling:"
@@ -229,13 +231,13 @@ class lora_config_model(BaseModel):
     )
 
 
-class sampling_config_model(BaseModel):
+class SamplingConfig(BaseModel):
     override_preset: Optional[str] = Field(
         None, description=("Select a sampler override preset")
     )
 
 
-class developer_config_model(BaseModel):
+class DeveloperConfig(BaseModel):
     unsafe_launch: Optional[bool] = Field(
         False, description=("Skip Exllamav2 version check")
     )
@@ -257,7 +259,7 @@ class developer_config_model(BaseModel):
     )
 
 
-class embeddings_config_model(BaseModel):
+class EmbeddingsConfig(BaseModel):
     embedding_model_dir: Optional[str] = Field(
         "models",
         description=(
@@ -276,18 +278,20 @@ class embeddings_config_model(BaseModel):
     )
 
 
-class tabby_config_model(BaseModel):
-    config: config_config_model = Field(default_factory=config_config_model)
-    network: network_config_model = Field(default_factory=network_config_model)
-    logging: logging_config_model = Field(default_factory=logging_config_model)
-    model: model_config_model = Field(default_factory=model_config_model)
-    draft_model: draft_model_config_model = Field(
-        default_factory=draft_model_config_model
+class TabbyConfigModel(BaseModel):
+    config: ConfigConfig = Field(default_factory=ConfigConfig.model_construct)
+    network: NetworkConfig = Field(default_factory=NetworkConfig.model_construct)
+    logging: LoggingConfig = Field(default_factory=LoggingConfig.model_construct)
+    model: ModelConfig = Field(default_factory=ModelConfig.model_construct)
+    draft_model: DraftModelConfig = Field(
+        default_factory=DraftModelConfig.model_construct
+    )
+    lora: LoraConfig = Field(default_factory=LoraConfig.model_construct)
+    sampling: SamplingConfig = Field(default_factory=SamplingConfig.model_construct)
+    developer: DeveloperConfig = Field(default_factory=DeveloperConfig.model_construct)
+    embeddings: EmbeddingsConfig = Field(
+        default_factory=EmbeddingsConfig.model_construct
     )
-    lora: lora_config_model = Field(default_factory=lora_config_model)
-    sampling: sampling_config_model = Field(default_factory=sampling_config_model)
-    developer: developer_config_model = Field(default_factory=developer_config_model)
-    embeddings: embeddings_config_model = Field(default_factory=embeddings_config_model)
 
     @model_validator(mode="before")
     def set_defaults(cls, values):
@@ -297,11 +301,11 @@ class tabby_config_model(BaseModel):
                 values[field_name] = cls.__annotations__[field_name](**default_instance)
         return values
 
-    model_config = ConfigDict(validate_assignment=True)
+    model_config = ConfigDict(validate_assignment=True, protected_namespaces=())
 
 
 def generate_config_file(filename="config_sample.yml", indentation=2):
-    schema = tabby_config_model.model_json_schema()
+    schema = TabbyConfigModel.model_json_schema()
 
     def dump_def(id: str, indent=2):
         yaml = ""