Moved model backends to separate folders

added some model backend settings save/load

modeling/inference_models/readonly/class.py

@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+import torch
+import requests
+import numpy as np
+from typing import List, Optional, Union
+
+import utils
+from logger import logger
+from modeling.inference_model import (
+    GenerationResult,
+    GenerationSettings,
+    InferenceModel,
+    ModelCapabilities,
+)
+
+model_backend_name = "Read Only"
+
+class BasicAPIException(Exception):
+    """To be used for errors when using the Basic API as an interface."""
+
+
+class model_backend(InferenceModel):
+    def __init__(self) -> None:
+        super().__init__()
+
+        # Do not allow API to be served over the API
+        self.capabilties = ModelCapabilities(api_host=False)
+        self.tokenizer = self._tokenizer()
+        self.model = None
+        self.model_name = "Read Only"
+    
+    def is_valid(self, model_name, model_path, menu_path):
+        return model_name == "ReadOnly"
+    
+    def get_requested_parameters(self, model_name, model_path, menu_path):
+        requested_parameters = []
+        return requested_parameters
+        
+    def set_input_parameters(self, parameters):
+        return
+
+    def unload(self):
+        utils.koboldai_vars.noai = False
+
+    def _initialize_model(self):
+        return
+    
+    class _tokenizer():
+        def __init__(self):
+            self._koboldai_header = []
+        def decode(self, _input):
+            return ""
+        def encode(self, input_text):
+            return []
+
+    def _load(self, save_model: bool = False, initial_load: bool = False) -> None:
+        self.tokenizer = self.tokenizer
+        self.model = None
+        utils.koboldai_vars.noai = True
+
+    def _raw_generate(
+        self,
+        prompt_tokens: Union[List[int], torch.Tensor],
+        max_new: int,
+        gen_settings: GenerationSettings,
+        single_line: bool = False,
+        batch_count: int = 1,
+        seed: Optional[int] = None,
+        **kwargs,
+    ):
+        return GenerationResult(
+            model=self,
+            out_batches=np.array([]),
+            prompt=prompt_tokens,
+            is_whole_generation=True,
+            single_line=single_line,
+        )