diff --git a/tpu_mtj_backend.py b/tpu_mtj_backend.py
index d292de0e..02754d95 100644
--- a/tpu_mtj_backend.py
+++ b/tpu_mtj_backend.py
@@ -1461,48 +1461,48 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
     with torch_lazy_loader.use_lazy_torch_load(callback=callback, dematerialized_modules=True):
         if(os.path.isdir(koboldai_vars.custmodpth)):
             try:
-                tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache", use_fast=False)
+                tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
             except Exception as e:
                 try:
-                    tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
+                    tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
                 except Exception as e:
                     try:
-                        tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
+                        tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
                     except Exception as e:
-                        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
+                        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
             try:
-                model     = AutoModelForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
+                model     = AutoModelForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
             except Exception as e:
-                model     = GPTNeoForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
+                model     = GPTNeoForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
         elif(os.path.isdir("models/{}".format(koboldai_vars.model.replace('/', '_')))):
             try:
-                tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache", use_fast=False)
+                tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
             except Exception as e:
                 try:
-                    tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache")
+                    tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
                 except Exception as e:
                     try:
-                        tokenizer = GPT2Tokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache")
+                        tokenizer = GPT2Tokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
                     except Exception as e:
-                        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
+                        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
             try:
-                model     = AutoModelForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache")
+                model     = AutoModelForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
             except Exception as e:
-                model     = GPTNeoForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache")
+                model     = GPTNeoForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
         else:
             try:
-                tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=args.revision, cache_dir="cache", use_fast=False)
+                tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
             except Exception as e:
                 try:
-                    tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=args.revision, cache_dir="cache")
+                    tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
                 except Exception as e:
                     try:
-                        tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.model, revision=args.revision, cache_dir="cache")
+                        tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
                     except Exception as e:
-                        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
+                        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
             try:
-                model     = AutoModelForCausalLM.from_pretrained(koboldai_vars.model, revision=args.revision, cache_dir="cache")
+                model     = AutoModelForCausalLM.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
             except Exception as e:
-                model     = GPTNeoForCausalLM.from_pretrained(koboldai_vars.model, revision=args.revision, cache_dir="cache")
+                model     = GPTNeoForCausalLM.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
 
     #network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))