diff --git a/aiserver.py b/aiserver.py
index ef3ec18e..38b50846 100644
--- a/aiserver.py
+++ b/aiserver.py
@@ -10,6 +10,8 @@ import eventlet
 eventlet.monkey_patch(all=True, thread=False)
 import os
 os.system("")
+__file__ = os.path.dirname(os.path.realpath(__file__))
+os.chdir(__file__)
 os.environ['EVENTLET_THREADPOOL_SIZE'] = '1'
 os.environ['TOKENIZERS_PARALLELISM'] = 'false'
 from eventlet import tpool
@@ -1024,7 +1026,7 @@ log.setLevel(logging.ERROR)
 print("{0}Initializing Flask... {1}".format(colors.PURPLE, colors.END), end="")
 from flask import Flask, render_template, Response, request, copy_current_request_context
 from flask_socketio import SocketIO, emit
-app = Flask(__name__)
+app = Flask(__name__, root_path=os.getcwd())
 app.config['SECRET KEY'] = 'secret!'
 socketio = SocketIO(app, async_method="eventlet")
 print("{0}OK!{1}".format(colors.GREEN, colors.END))
@@ -1101,7 +1103,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go
 
             return lazy_load_callback
 
-        lazy_load_config_path = os.path.join(path.dirname(path.realpath(__file__)), "maps", vars.model_type + ".json")
+        lazy_load_config_path = os.path.join("maps", vars.model_type + ".json")
         if(vars.lazy_load and "model_config" in globals() and os.path.isfile(lazy_load_config_path)):
             with open(lazy_load_config_path) as f:
                 lazy_load_spec = json.load(f)
@@ -2193,16 +2195,16 @@ vars.lua_state = lupa.LuaRuntime(unpack_returned_tuples=True)
 
 # Load bridge.lua
 bridged = {
-    "corescript_path": os.path.join(os.path.dirname(os.path.realpath(__file__)), "cores"),
-    "userscript_path": os.path.join(os.path.dirname(os.path.realpath(__file__)), "userscripts"),
-    "config_path": os.path.join(os.path.dirname(os.path.realpath(__file__)), "userscripts"),
-    "lib_paths": vars.lua_state.table(os.path.join(os.path.dirname(os.path.realpath(__file__)), "lualibs"), os.path.join(os.path.dirname(os.path.realpath(__file__)), "extern", "lualibs")),
+    "corescript_path": "cores",
+    "userscript_path": "userscripts",
+    "config_path": "userscripts",
+    "lib_paths": vars.lua_state.table("lualibs", os.path.join("extern", "lualibs")),
     "vars": vars,
 }
 for kwarg in _bridged:
     bridged[kwarg] = _bridged[kwarg]
 try:
-    vars.lua_kobold, vars.lua_koboldcore, vars.lua_koboldbridge = vars.lua_state.globals().dofile(os.path.join(os.path.dirname(os.path.realpath(__file__)), "bridge.lua"))(
+    vars.lua_kobold, vars.lua_koboldcore, vars.lua_koboldbridge = vars.lua_state.globals().dofile("bridge.lua")(
         vars.lua_state.globals().python,
         bridged,
     )
diff --git a/fileops.py b/fileops.py
index 5453b04f..cd269931 100644
--- a/fileops.py
+++ b/fileops.py
@@ -65,30 +65,30 @@ def getdirpath(dir, title):
 #  Returns the path (as a string) to the given story by its name
 #==================================================================#
 def storypath(name):
-    return path.join(path.dirname(path.realpath(__file__)), "stories", name + ".json")
+    return path.join("stories", name + ".json")
 
 #==================================================================#
 #  Returns the path (as a string) to the given soft prompt by its filename
 #==================================================================#
 def sppath(filename):
-    return path.join(path.dirname(path.realpath(__file__)), "softprompts", filename)
+    return path.join("softprompts", filename)
 
 #==================================================================#
 #  Returns the path (as a string) to the given username by its filename
 #==================================================================#
 def uspath(filename):
-    return path.join(path.dirname(path.realpath(__file__)), "userscripts", filename)
+    return path.join("userscripts", filename)
 
 #==================================================================#
 #  Returns an array of dicts containing story files in /stories
 #==================================================================#
 def getstoryfiles():
     list = []
-    for file in listdir(path.dirname(path.realpath(__file__))+"/stories"):
+    for file in listdir("stories"):
         if file.endswith(".json"):
             ob = {}
             ob["name"] = file.replace(".json", "")
-            f = open(path.dirname(path.realpath(__file__))+"/stories/"+file, "r")
+            f = open("stories/"+file, "r")
             try:
                 js = json.load(f)
             except:
@@ -112,7 +112,7 @@ def checksp(filename: str, model_dimension: int) -> Tuple[Union[zipfile.ZipFile,
     if 'np' not in globals():
         import numpy as np
     try:
-        z = zipfile.ZipFile(path.dirname(path.realpath(__file__))+"/softprompts/"+filename)
+        z = zipfile.ZipFile("softprompts/"+filename)
         with z.open('tensor.npy') as f:
             # Read only the header of the npy file, for efficiency reasons
             version: Tuple[int, int] = np.lib.format.read_magic(f)
@@ -140,8 +140,8 @@ def checksp(filename: str, model_dimension: int) -> Tuple[Union[zipfile.ZipFile,
 #==================================================================#
 def getspfiles(model_dimension: int):
     lst = []
-    os.makedirs(path.dirname(path.realpath(__file__))+"/softprompts", exist_ok=True)
-    for file in listdir(path.dirname(path.realpath(__file__))+"/softprompts"):
+    os.makedirs("softprompts", exist_ok=True)
+    for file in listdir("softprompts"):
         if not file.endswith(".zip"):
             continue
         z, version, shape, fortran_order, dtype = checksp(file, model_dimension)
@@ -174,8 +174,8 @@ def getspfiles(model_dimension: int):
 #==================================================================#
 def getusfiles(long_desc=False):
     lst = []
-    os.makedirs(path.dirname(path.realpath(__file__))+"/userscripts", exist_ok=True)
-    for file in listdir(path.dirname(path.realpath(__file__))+"/userscripts"):
+    os.makedirs("userscripts", exist_ok=True)
+    for file in listdir("userscripts"):
         if file.endswith(".lua"):
             ob = {}
             ob["filename"] = file
diff --git a/tpu_mtj_backend.py b/tpu_mtj_backend.py
index a565b578..b13e3aa3 100644
--- a/tpu_mtj_backend.py
+++ b/tpu_mtj_backend.py
@@ -822,7 +822,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
 
     # Try to convert HF config.json to MTJ config
     if hf_checkpoint:
-        spec_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "maps", vars.model_type + ".json")
+        spec_path = os.path.join("maps", vars.model_type + ".json")
         if not os.path.isfile(spec_path):
             raise NotImplementedError(f"Unsupported model type {repr(vars.model_type)}")
         with open(spec_path) as f:
@@ -1035,29 +1035,38 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
         if(os.path.isdir(vars.custmodpth)):
             try:
                 tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, cache_dir="cache")
-            except ValueError as e:
-                tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, cache_dir="cache")
+            except Exception as e:
+                try:
+                    tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, cache_dir="cache")
+                except Exception as e:
+                    tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", cache_dir="cache")
             try:
                 model     = AutoModelForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache")
-            except ValueError as e:
+            except Exception as e:
                 model     = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache")
         elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
             try:
                 tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
-            except ValueError as e:
-                tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
+            except Exception as e:
+                try:
+                    tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
+                except Exception as e:
+                    tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", cache_dir="cache")
             try:
                 model     = AutoModelForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
-            except ValueError as e:
+            except Exception as e:
                 model     = GPTNeoForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
         else:
             try:
                 tokenizer = AutoTokenizer.from_pretrained(vars.model, cache_dir="cache")
-            except ValueError as e:
-                tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, cache_dir="cache")
+            except Exception as e:
+                try:
+                    tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, cache_dir="cache")
+                except Exception as e:
+                    tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", cache_dir="cache")
             try:
                 model     = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache")
-            except ValueError as e:
+            except Exception as e:
                 model     = GPTNeoForCausalLM.from_pretrained(vars.model, cache_dir="cache")
 
     network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))