diff --git a/aiserver.py b/aiserver.py
index d046a437..2c554174 100644
--- a/aiserver.py
+++ b/aiserver.py
@@ -82,6 +82,7 @@ class vars:
     submission  = ""     # Same as above, but after applying input formatting
     lastctx     = ""     # The last context submitted to the generator
     model       = ""     # Model ID string chosen at startup
+    model_type  = ""     # Model Type (Automatically taken from the model config)
     noai        = False  # Runs the script without starting up the transformers pipeline
     aibusy      = False  # Stops submissions while the AI is working
     max_length  = 1024    # Maximum number of tokens to submit per action
@@ -388,9 +389,29 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
     vars.allowsp = True
     # Test for GPU support
     import torch
+    
+    # Make model path the same as the model name to make this consistent with the other loading method if it isn't a known model type
+    # This code is not just a workaround for below, it is also used to make the behavior consistent with other loading methods - Henk717
+    if(not vars.model in ["NeoCustom", "GPT2Custom"]):
+        vars.custmodpth = vars.model.replace('/', '_')
+    # Get the model_type from the config or assume a model type if it isn't present
+    from transformers import AutoConfig
+    try:
+        model_config = AutoConfig.from_pretrained(vars.custmodpth)  
+    except ValueError as e:
+        vars.model_type = "not_found" 
+    if(not vars.model_type == "not_found"):
+        vars.model_type = model_config.model_type
+    elif(vars.model == "NeoCustom"):
+        vars.model_type = "gpt_neo"
+    elif(vars.model == "GPT2Custom"):
+        vars.model_type = "gpt2"
+    else:
+        print("WARNING: No model type detected, assuming Neo (If this is a GPT2 model use the other menu option or --model GPT2Custom)")
+        vars.model_type = "gpt_neo"
     print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="")
     vars.hascuda = torch.cuda.is_available()
-    vars.bmsupported = vars.model in ("EleutherAI/gpt-neo-1.3B", "EleutherAI/gpt-neo-2.7B", "EleutherAI/gpt-j-6B", "NeoCustom")
+    vars.bmsupported = vars.model_type in ("gpt_neo", "gptj")
     if(args.breakmodel is not None and args.breakmodel):
         print("WARNING: --breakmodel is no longer supported. Breakmodel mode is now automatically enabled when --layers is used (see --help for details).", file=sys.stderr)
     if(args.breakmodel_layers is not None):
@@ -864,9 +885,6 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
             # feature yet
             if("/" not in vars.model and vars.model.lower().startswith("gpt2")):
                 lowmem = {}
-
-            # Make model path the same as the model name to make this consistent with the other loading method
-            vars.custmodpth = vars.model.replace('/', '_')
             
             # Download model from Huggingface if it does not exist, otherwise load locally
             
@@ -874,6 +892,7 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
                with(maybe_use_float16()):
                    tokenizer = GPT2TokenizerFast.from_pretrained(vars.model.replace('/', '_'), cache_dir="cache/")
                    model = AutoModelForCausalLM.from_pretrained(vars.model.replace('/', '_'), cache_dir="cache/", **lowmem)
+                   
             else:
                 print("Model does not exist locally, attempting to download from Huggingface...")
                 tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, cache_dir="cache/")
@@ -884,7 +903,7 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
                 shutil.rmtree("cache/")
                 model.save_pretrained(vars.model.replace('/', '_'))
                 tokenizer.save_pretrained(vars.model.replace('/', '_'))
-
+            
             if(vars.hascuda):
                 if(vars.usegpu):
                     vars.modeldim = get_hidden_size_from_model(model)