Model: Fix gpu split params

GPU split auto is a bool and GPU split is an array of integers for
GBs to allocate per GPU.

Signed-off-by: kingbri <bdashore3@proton.me>

OAI/types/model.py

@@ -15,7 +15,8 @@ class ModelList(BaseModel):
 class ModelLoadRequest(BaseModel):
     name: str
     max_seq_len: Optional[int] = 4096
-    gpu_split: Optional[str] = "auto"
+    gpu_split_auto: Optional[bool] = True
+    gpu_split: Optional[List[float]] = Field(default_factory=list)
     rope_scale: Optional[float] = 1.0
     rope_alpha: Optional[float] = 1.0
     no_flash_attention: Optional[bool] = False

main.py

@@ -139,8 +139,6 @@ if __name__ == "__main__":
                 loading_bar.finish()
             else:
                 loading_bar.next()
-    
-        print("Model successfully loaded.")
 
     network_config = config["network"]
     uvicorn.run(app, host=network_config["host"] or "127.0.0.1", port=network_config["port"] or 8012, log_level="debug")

model.py

@@ -55,7 +55,7 @@ class ModelContainer:
                     By default, the draft model's alpha value is calculated automatically to scale to the size of the
                     full model.
                 'gpu_split_auto' (bool): Automatically split model across available devices (default: True)
-                'gpu_split' (list): Allocation for weights and (some) tensors, per device
+                'gpu_split' (list[float]): Allocation for weights and (some) tensors, per device
                 'no_flash_attn' (bool): Turns off flash attention (increases vram usage)
         """
 
@@ -63,7 +63,7 @@ class ModelContainer:
 
         self.cache_fp8 = "cache_mode" in kwargs and kwargs["cache_mode"] == "FP8"
         self.gpu_split = kwargs.get("gpu_split", None)
-        self.gpu_split_auto = self.gpu_split == "auto"
+        self.gpu_split_auto = kwargs.get("gpu_split_auto", True)
 
         self.config = ExLlamaV2Config()
         self.config.model_dir = str(model_directory.resolve())
@@ -177,6 +177,8 @@ class ModelContainer:
 
         self.generator = ExLlamaV2StreamingGenerator(self.model, self.cache, self.tokenizer, self.draft_model, self.draft_cache)
 
+        print("Model successfully loaded.")
+
 
     def unload(self):
         """