diff --git a/aiserver.py b/aiserver.py
index 68190b37..cd4854ba 100644
--- a/aiserver.py
+++ b/aiserver.py
@@ -1337,6 +1337,8 @@ def processsettings(js):
         koboldai_vars.chatmode = js["chatmode"]
     if("chatname" in js):
         koboldai_vars.chatname = js["chatname"]
+    if("botname" in js):
+        koboldai_vars.botname = js["botname"]
     if("dynamicscan" in js):
         koboldai_vars.dynamicscan = js["dynamicscan"]
     if("nopromptgen" in js):
@@ -1783,15 +1785,15 @@ def get_layer_count(model, directory=""):
                 model = directory
             from transformers import AutoConfig
             if(os.path.isdir(model.replace('/', '_'))):
-                model_config = AutoConfig.from_pretrained(model.replace('/', '_'), revision=args.revision, cache_dir="cache")
+                model_config = AutoConfig.from_pretrained(model.replace('/', '_'), revision=koboldai_vars.revision, cache_dir="cache")
             elif(is_model_downloaded(model)):
-                model_config = AutoConfig.from_pretrained("models/{}".format(model.replace('/', '_')), revision=args.revision, cache_dir="cache")
+                model_config = AutoConfig.from_pretrained("models/{}".format(model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
             elif(os.path.isdir(directory)):
-                model_config = AutoConfig.from_pretrained(directory, revision=args.revision, cache_dir="cache")
+                model_config = AutoConfig.from_pretrained(directory, revision=koboldai_vars.revision, cache_dir="cache")
             elif(os.path.isdir(koboldai_vars.custmodpth.replace('/', '_'))):
-                model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth.replace('/', '_'), revision=args.revision, cache_dir="cache")
+                model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth.replace('/', '_'), revision=koboldai_vars.revision, cache_dir="cache")
             else:
-                model_config = AutoConfig.from_pretrained(model, revision=args.revision, cache_dir="cache")
+                model_config = AutoConfig.from_pretrained(model, revision=koboldai_vars.revision, cache_dir="cache")
         try:
             if ((utils.HAS_ACCELERATE and model_config.model_type != 'gpt2') or model_config.model_type in ("gpt_neo", "gptj", "xglm", "opt")) and not koboldai_vars.nobreakmodel:
                 return utils.num_layers(model_config)
@@ -2764,19 +2766,19 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
         from transformers import AutoConfig
         if(os.path.isdir(koboldai_vars.custmodpth.replace('/', '_'))):
             try:
-                model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth.replace('/', '_'), revision=args.revision, cache_dir="cache")
+                model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth.replace('/', '_'), revision=koboldai_vars.revision, cache_dir="cache")
                 koboldai_vars.model_type = model_config.model_type
             except ValueError as e:
                 koboldai_vars.model_type = "not_found"
         elif(os.path.isdir("models/{}".format(koboldai_vars.custmodpth.replace('/', '_')))):
             try:
-                model_config = AutoConfig.from_pretrained("models/{}".format(koboldai_vars.custmodpth.replace('/', '_')), revision=args.revision, cache_dir="cache")
+                model_config = AutoConfig.from_pretrained("models/{}".format(koboldai_vars.custmodpth.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
                 koboldai_vars.model_type = model_config.model_type
             except ValueError as e:
                 koboldai_vars.model_type = "not_found"
         else:
             try:
-                model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
+                model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
                 koboldai_vars.model_type = model_config.model_type
             except ValueError as e:
                 koboldai_vars.model_type = "not_found"
@@ -2876,7 +2878,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
         
         print(tokenizer_id, koboldai_vars.newlinemode)
 
-        tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache")
+        tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache")
 
         loadsettings()
         koboldai_vars.colaburl = url or koboldai_vars.colaburl
@@ -3061,19 +3063,19 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
                 with(maybe_use_float16()):
                     try:
                         if os.path.exists(koboldai_vars.custmodpth):
-                            model = GPT2LMHeadModel.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
-                            tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
+                            model = GPT2LMHeadModel.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
+                            tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
                         elif os.path.exists(os.path.join("models/", koboldai_vars.custmodpth)):
-                            model = GPT2LMHeadModel.from_pretrained(os.path.join("models/", koboldai_vars.custmodpth), revision=args.revision, cache_dir="cache")
-                            tokenizer = GPT2Tokenizer.from_pretrained(os.path.join("models/", koboldai_vars.custmodpth), revision=args.revision, cache_dir="cache")
+                            model = GPT2LMHeadModel.from_pretrained(os.path.join("models/", koboldai_vars.custmodpth), revision=koboldai_vars.revision, cache_dir="cache")
+                            tokenizer = GPT2Tokenizer.from_pretrained(os.path.join("models/", koboldai_vars.custmodpth), revision=koboldai_vars.revision, cache_dir="cache")
                         else:
-                            model = GPT2LMHeadModel.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
-                            tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
+                            model = GPT2LMHeadModel.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
+                            tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
                     except Exception as e:
                         if("out of memory" in traceback.format_exc().lower()):
                             raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
                         raise e
-                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")
                 model.save_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), max_shard_size="500MiB")
                 tokenizer.save_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')))
                 koboldai_vars.modeldim = get_hidden_size_from_model(model)
@@ -3120,38 +3122,38 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
                         lowmem = {}
                     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", **lowmem)
+                            model     = AutoModelForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
                         except Exception as e:
                             if("out of memory" in traceback.format_exc().lower()):
                                 raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
-                            model     = GPTNeoForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache", **lowmem)
+                            model     = GPTNeoForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
                     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", **lowmem)
+                            model     = AutoModelForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
                         except Exception as e:
                             if("out of memory" in traceback.format_exc().lower()):
                                 raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
-                            model     = GPTNeoForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache", **lowmem)
+                            model     = GPTNeoForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
                     else:
                         old_rebuild_tensor = torch._utils._rebuild_tensor
                         def new_rebuild_tensor(storage: Union[torch_lazy_loader.LazyTensor, torch.Storage], storage_offset, shape, stride):
@@ -3167,21 +3169,21 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
                         torch._utils._rebuild_tensor = new_rebuild_tensor
 
                         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", **lowmem)
+                            model     = AutoModelForCausalLM.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
                         except Exception as e:
                             if("out of memory" in traceback.format_exc().lower()):
                                 raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
-                            model     = GPTNeoForCausalLM.from_pretrained(koboldai_vars.model, revision=args.revision, cache_dir="cache", **lowmem)
+                            model     = GPTNeoForCausalLM.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
 
                         torch._utils._rebuild_tensor = old_rebuild_tensor
 
@@ -3198,13 +3200,13 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
                                 import huggingface_hub
                                 legacy = packaging.version.parse(transformers_version) < packaging.version.parse("4.22.0.dev0")
                                 # Save the config.json
-                                shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, transformers.configuration_utils.CONFIG_NAME, revision=args.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), transformers.configuration_utils.CONFIG_NAME))
+                                shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, transformers.configuration_utils.CONFIG_NAME, revision=koboldai_vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), transformers.configuration_utils.CONFIG_NAME))
                                 if(utils.num_shards is None):
                                     # Save the pytorch_model.bin of an unsharded model
                                     try:
-                                        shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, transformers.modeling_utils.WEIGHTS_NAME, revision=args.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_NAME))
+                                        shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, transformers.modeling_utils.WEIGHTS_NAME, revision=koboldai_vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_NAME))
                                     except:
-                                        shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model,  "model.safetensors", revision=args.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), "model.safetensors"))
+                                        shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model,  "model.safetensors", revision=koboldai_vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), "model.safetensors"))
                                 else:
                                     with open(utils.from_pretrained_index_filename) as f:
                                         map_data = json.load(f)
@@ -3213,7 +3215,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
                                     shutil.move(os.path.realpath(utils.from_pretrained_index_filename), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_INDEX_NAME))
                                     # Then save the pytorch_model-#####-of-#####.bin files
                                     for filename in filenames:
-                                        shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, filename, revision=args.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), filename))
+                                        shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, filename, revision=koboldai_vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), filename))
                             shutil.rmtree("cache/")
 
                 if(koboldai_vars.badwordsids is koboldai_settings.badwordsids_default and koboldai_vars.model_type not in ("gpt2", "gpt_neo", "gptj")):
@@ -3259,7 +3261,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
         
         else:
             from transformers import GPT2Tokenizer
-            tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
+            tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
     else:
         from transformers import PreTrainedModel
         from transformers import modeling_utils
@@ -3672,7 +3674,7 @@ def lua_decode(tokens):
     if("tokenizer" not in globals()):
         from transformers import GPT2Tokenizer
         global tokenizer
-        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
+        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
     return utils.decodenewlines(tokenizer.decode(tokens))
 
 #==================================================================#
@@ -3684,7 +3686,7 @@ def lua_encode(string):
     if("tokenizer" not in globals()):
         from transformers import GPT2Tokenizer
         global tokenizer
-        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
+        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
     return tokenizer.encode(utils.encodenewlines(string), max_length=int(4e9), truncation=True)
 
 #==================================================================#
@@ -3858,6 +3860,7 @@ def lua_has_setting(setting):
         "useprompt",
         "chatmode",
         "chatname",
+        "botname",
         "adventure",
         "dynamicscan",
         "nopromptgen",
@@ -4174,6 +4177,7 @@ def do_connect():
         return
     logger.debug("{0}Client connected!{1}".format(colors.GREEN, colors.END))
     emit('from_server', {'cmd': 'setchatname', 'data': koboldai_vars.chatname}, room="UI_1")
+    emit('from_server', {'cmd': 'setbotname', 'data': koboldai_vars.botname}, room="UI_1")
     emit('from_server', {'cmd': 'setanotetemplate', 'data': koboldai_vars.authornotetemplate}, room="UI_1")
     emit('from_server', {'cmd': 'connected', 'smandelete': koboldai_vars.smandelete, 'smanrename': koboldai_vars.smanrename, 'modelname': getmodelname()}, room="UI_1")
     if(koboldai_vars.host):
@@ -4239,8 +4243,10 @@ def get_message(msg):
                 if(type(msg['chatname']) is not str):
                     raise ValueError("Chatname must be a string")
                 koboldai_vars.chatname = msg['chatname']
+                koboldai_vars.botname = msg['botname']
                 settingschanged()
                 emit('from_server', {'cmd': 'setchatname', 'data': koboldai_vars.chatname}, room="UI_1")
+                emit('from_server', {'cmd': 'setbotname', 'data': koboldai_vars.botname}, room="UI_1")
             koboldai_vars.recentrng = koboldai_vars.recentrngm = None
             actionsubmit(msg['data'], actionmode=msg['actionmode'])
         elif(koboldai_vars.mode == "edit"):
@@ -4258,8 +4264,10 @@ def get_message(msg):
             if(type(msg['chatname']) is not str):
                 raise ValueError("Chatname must be a string")
             koboldai_vars.chatname = msg['chatname']
+            koboldai_vars.botname = msg['botname']
             settingschanged()
             emit('from_server', {'cmd': 'setchatname', 'data': koboldai_vars.chatname}, room="UI_1")
+            emit('from_server', {'cmd': 'setbotname', 'data': koboldai_vars.botname}, room="UI_1")
         actionretry(msg['data'])
     # Back/Undo Action
     elif(msg['cmd'] == 'back'):
@@ -4842,19 +4850,19 @@ def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False,
                 try:
                     if(os.path.isdir(tokenizer_id)):
                         try:
-                            tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache")
+                            tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache")
                         except:
-                            tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache", use_fast=False)
+                            tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
                     elif(os.path.isdir("models/{}".format(tokenizer_id.replace('/', '_')))):
                         try:
-                            tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=args.revision, cache_dir="cache")
+                            tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
                         except:
-                            tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=args.revision, cache_dir="cache", use_fast=False)
+                            tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
                     else:
                         try:
-                            tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache")
+                            tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache")
                         except:
-                            tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache", use_fast=False)
+                            tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
                 except:
                     logger.warning(f"Unknown tokenizer {repr(tokenizer_id)}")
                 koboldai_vars.api_tokenizer_id = tokenizer_id
@@ -4875,9 +4883,13 @@ def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False,
         
         # "Chat" mode
         if(koboldai_vars.chatmode and koboldai_vars.gamestarted):
+            if(koboldai_vars.botname):
+                botname = (koboldai_vars.botname + ":")
+            else:
+                botname = ""
             data = re.sub(r'\n+', ' ', data)
             if(len(data)):
-                data = f"\n{koboldai_vars.chatname}: {data}\n"
+                data = f"\n{koboldai_vars.chatname}: {data}\n{botname}"
         
         # If we're not continuing, store a copy of the raw input
         if(data != ""):
@@ -5229,7 +5241,7 @@ def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions, submission=None,
     if("tokenizer" not in globals()):
         from transformers import GPT2Tokenizer
         global tokenizer
-        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
+        tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
 
     lnheader = len(tokenizer._koboldai_header)
 
@@ -7162,8 +7174,8 @@ def exitModes():
 #  Launch in-browser save prompt
 #==================================================================#
 def saveas(data):
-    
-    koboldai_vars.story_name = data['name']
+    name = data['name']
+    koboldai_vars.story_name = name
     if not data['pins']:
         koboldai_vars.actions.clear_all_options()
     # Check if filename exists already
diff --git a/colab/TPU.ipynb b/colab/TPU.ipynb
index 60664780..9e2375e2 100644
--- a/colab/TPU.ipynb
+++ b/colab/TPU.ipynb
@@ -66,7 +66,7 @@
         "#@title <b><-- Select your model below and then click this to start KoboldAI</b>\n",
         "#@markdown You can find a description of the models below along with instructions on how to start KoboldAI.\n",
         "\n",
-        "Model = \"Nerys 13B V2\" #@param [\"Nerys 13B V2\", \"Erebus 13B\", \"Janeway 13B\", \"Shinen 13B\", \"Skein 20B\", \"Erebus 20B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Pygmalion 6B\", \"Pygmalion 6B Dev\", \"Lit V2 6B\", \"Lit 6B\", \"NeoX 20B\", \"OPT 13B\", \"Fairseq Dense 13B\", \"GPT-J-6B\"] {allow-input: true}\n",
+        "Model = \"Nerys 13B V2\" #@param [\"Nerys 13B V2\", \"Erebus 13B\", \"Janeway 13B\", \"Shinen 13B\", \"Skein 20B\", \"Erebus 20B\", \"Skein 6B\", \"Janeway 6B\", \"Adventure 6B\", \"Shinen 6B\", \"Pygmalion 6B\", \"Lit V2 6B\", \"Lit 6B\", \"NeoX 20B\", \"OPT 13B\", \"Fairseq Dense 13B\", \"GPT-J-6B\"] {allow-input: true}\n",
         "Version = \"Official\" #@param [\"Official\", \"United\"] {allow-input: true}\n",
         "Provider = \"Cloudflare\" #@param [\"Localtunnel\", \"Cloudflare\"]\n",
         "use_google_drive = True #@param {type:\"boolean\"}\n",
diff --git a/koboldai_settings.py b/koboldai_settings.py
index 674e00ae..d85e2919 100644
--- a/koboldai_settings.py
+++ b/koboldai_settings.py
@@ -649,7 +649,7 @@ class model_settings(settings):
     no_save_variables = ['modelconfig', 'custmodpth', 'generated_tkns', 
                          'loaded_layers', 'total_layers', 'total_download_chunks', 'downloaded_chunks', 'presets', 'default_preset', 
                          'welcome', 'welcome_default', 'simple_randomness', 'simple_creativity', 'simple_repitition',
-                         'badwordsids', 'uid_presets', 'revision', 'model', 'model_type', 'lazy_load', 'fp32_model', 'modeldim', 'horde_wait_time', 'horde_queue_position', 'horde_queue_size', 'newlinemode', 'tqdm_progress', 'tqdm_rem_time', '_tqdm']
+                         'badwordsids', 'uid_presets', 'model', 'model_type', 'lazy_load', 'fp32_model', 'modeldim', 'horde_wait_time', 'horde_queue_position', 'horde_queue_size', 'newlinemode', 'tqdm_progress', 'tqdm_rem_time', '_tqdm']
     settings_name = "model"
     default_settings = {"rep_pen" : 1.1, "rep_pen_slope": 0.7, "rep_pen_range": 1024, "temp": 0.5, "top_p": 0.9, "top_k": 0, "top_a": 0.0, "tfs": 1.0, "typical": 1.0,
                         "sampler_order": [6,0,1,2,3,4,5]}
@@ -707,7 +707,6 @@ class model_settings(settings):
         self.sampler_order = [6, 0, 1, 2, 3, 4, 5]
         self.newlinemode = "n"
         self.lazy_load   = True # Whether or not to use torch_lazy_loader.py for transformers models in order to reduce CPU memory usage
-        self.revision    = None
         self.presets     = []   # Holder for presets
         self.selected_preset = ""
         self.uid_presets = []
@@ -871,6 +870,7 @@ class story_settings(settings):
         self.useprompt   = False   # Whether to send the full prompt with every submit action
         self.chatmode    = False
         self.chatname    = "You"
+        self.botname    = "Bot"
         self.adventure   = False
         self.actionmode  = 0
         self.storymode   = 0
@@ -1124,7 +1124,7 @@ class story_settings(settings):
                 
 class user_settings(settings):
     local_only_variables = ['importjs']
-    no_save_variables = ['importnum', 'importjs', 'loadselect', 'spselect', 'svowname', 'saveow', 'laststory', 'sid']
+    no_save_variables = ['importnum', 'importjs', 'loadselect', 'spselect', 'svowname', 'saveow', 'laststory', 'sid', "revision"]
     settings_name = "user"
     def __init__(self, socketio):
         self._socketio = socketio
@@ -1171,6 +1171,7 @@ class user_settings(settings):
         self.screenshot_author_name = "Anonymous"
         self.screenshot_use_boring_colors = False
         self.oaiurl      = "" # OpenAI API URL
+        self.revision    = None
         self.oaiengines  = "https://api.openai.com/v1/engines"
         self.url         = "https://api.inferkit.com/v1/models/standard/generate" # InferKit API URL
         self.colaburl    = ""     # Ngrok url for Google Colab mode
diff --git a/static/application.js b/static/application.js
index 34a29066..df51b06e 100644
--- a/static/application.js
+++ b/static/application.js
@@ -946,8 +946,10 @@ function _dosubmit() {
 	submit_throttle = null;
 	input_text.val("");
 	hideMessage();
-	hidegenseqs();
-	socket.send({'cmd': 'submit', 'allowabort': !disallow_abort, 'actionmode': adventure ? action_mode : 0, 'chatname': chatmode ? chat_name.val() : undefined, 'data': txt});
+	if(!memorymode){
+		hidegenseqs();
+	}
+	socket.send({'cmd': 'submit', 'allowabort': !disallow_abort, 'actionmode': adventure ? action_mode : 0, 'chatname': chatmode ? chat_name.val() : undefined, 'botname': chatmode ? bot_name.val() : undefined, 'data': txt});
 }
 
 function changemode() {
@@ -1490,8 +1492,10 @@ function setmodevisibility(state) {
 function setchatnamevisibility(state) {
 	if(state){  // Enabling
 		show([chat_name]);
+		show([bot_name]);
 	} else{  // Disabling
 		hide([chat_name]);
+		hide([bot_name]);
 	}
 }
 
@@ -2264,6 +2268,7 @@ $(document).ready(function(){
 	input_text        = $('#input_text');
 	message_text      = $('#messagefield');
 	chat_name         = $('#chatname');
+	bot_name          = $('#botname');
 	settings_menu     = $("#settingsmenu");
 	format_menu       = $('#formatmenu');
 	anote_menu        = $('#anoterowcontainer');
@@ -2867,6 +2872,8 @@ $(document).ready(function(){
 			hidegenseqs();
 		} else if(msg.cmd == "setchatname") {
 			chat_name.val(msg.data);
+		} else if(msg.cmd == "setbotname") {
+			bot_name.val(msg.data);
 		} else if(msg.cmd == "setlabelnumseq") {
 			// Update setting label with value from server
 			$("#setnumseqcur").val(msg.data);
@@ -3181,7 +3188,7 @@ $(document).ready(function(){
 	button_actretry.on("click", function(ev) {
 		beginStream();
 		hideMessage();
-		socket.send({'cmd': 'retry', 'chatname': chatmode ? chat_name.val() : undefined, 'data': ''});
+		socket.send({'cmd': 'retry', 'chatname': chatmode ? chat_name.val() : undefined, 'botname': chatmode ? bot_name.val() : undefined, 'data': ''});
 		hidegenseqs();
 	});
 	
diff --git a/static/custom.css b/static/custom.css
index d4bfe872..3e266701 100644
--- a/static/custom.css
+++ b/static/custom.css
@@ -79,6 +79,13 @@ body.connected #topmenu, #topmenu.always-available {
 	margin-left: 10px;
 }
 
+#botname {
+	background-color: #404040;
+	color: #ffffff;
+	width: 200px;
+	margin-left: 10px;
+}
+
 #menuitems {
 	display: flex;
 	width: 100%;
diff --git a/templates/index.html b/templates/index.html
index d1995373..af99390f 100644
--- a/templates/index.html
+++ b/templates/index.html
@@ -144,6 +144,7 @@
 				<button type="button" class="btn btn-primary" id="btn_actretry">Retry</button>
 			</div>
 			<input type="text" id="chatname" class="form-control hidden" placeholder="Chat name">
+			<input type="text" id="botname" class="form-control hidden" placeholder="Bot name">
 			<div id="messagefield"></div>
 			<div class="box flex-push-right">
 				<input type="checkbox" data-toggle="toggle" data-onstyle="success" id="allowediting" disabled>
diff --git a/templates/settings flyout.html b/templates/settings flyout.html
index e7e41bd8..aae2335e 100644
--- a/templates/settings flyout.html	
+++ b/templates/settings flyout.html	
@@ -109,6 +109,20 @@
 				<span class="setting_minlabel"><span style="top: -4px; position: relative;"></span></span>
 				<span class="setting_maxlabel"><span style="top: -4px; position: relative;"></span></span>
 			</div>
+			<div class="setting_container chat_mode var_sync_alt_story_chatmode" ui_level=0>
+				<!---Top Row---->
+				<span class="setting_label">
+					<span style="white-space: pre-wrap;">Bot Name: </span>
+					<span class="helpicon material-icons-outlined" tooltip="The bot's name for chat mode.">help_icon</span>
+				</span>
+				<!---Bottom Row---->
+				<span class="setting_item" style="height: 25px;">
+					<input autocomplete="off" id="var_sync_story_botname" class="var_sync_story_botname settings_select" onchange="sync_to_server(this);">
+				</span>
+				<!---Slider Labels--->
+				<span class="setting_minlabel"><span style="top: -4px; position: relative;"></span></span>
+				<span class="setting_maxlabel"><span style="top: -4px; position: relative;"></span></span>
+			</div>
 		</div>
 		<span id="debug-dump" class="cursor" onclick="openPopup('debug-file-prompt');">Download debug dump</span>
 		<div id="Images">
diff --git a/templates/templates.html b/templates/templates.html
index 78aa4b14..4f16ff66 100644
--- a/templates/templates.html
+++ b/templates/templates.html
@@ -1,4 +1,5 @@
 <!---------------- World Info Card ---------------------->
+<link href="static/koboldai.css" rel="stylesheet">
 <div draggable="true" class="world_info_card" id="world_info_">
 	<div class="world_info_title_area">
 		<div>
@@ -24,7 +25,7 @@
 					contenteditable="true"
 					data-placeholder="Person"
 					spellcheck="false"
-				></span>
+				></span> <span class="helpicon material-icons-outlined" tooltip="Please enter a noun that describes a person, place or thing." "]">help_icon</span>
 			</div>
 		</div>
 		<span id="world_info_delete_" class="world_info_delete">X</span>
diff --git a/utils.py b/utils.py
index aa37028e..87ee77a5 100644
--- a/utils.py
+++ b/utils.py
@@ -286,7 +286,7 @@ def _transformers22_aria2_hook(pretrained_model_name_or_path: str, force_downloa
             if token is None:
                 raise EnvironmentError("You specified use_auth_token=True, but a huggingface token was not found.")
     _cache_dir = str(cache_dir) if cache_dir is not None else transformers.TRANSFORMERS_CACHE
-    _revision = revision if revision is not None else huggingface_hub.constants.DEFAULT_REVISION
+    _revision = koboldai_vars.revision if koboldai_vars.revision is not None else huggingface_hub.constants.DEFAULT_REVISION
     sharded = False
     headers = {"user-agent": transformers.file_utils.http_user_agent(user_agent)}
     if use_auth_token:
@@ -306,7 +306,7 @@ def _transformers22_aria2_hook(pretrained_model_name_or_path: str, force_downloa
             filename = transformers.modeling_utils.WEIGHTS_INDEX_NAME if sharded else transformers.modeling_utils.WEIGHTS_NAME
         except AttributeError:
             return
-        url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=revision)
+        url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=_revision)
         if is_cached(filename) or requests.head(url, allow_redirects=True, proxies=proxies, headers=headers):
             break
         if sharded:
@@ -320,7 +320,7 @@ def _transformers22_aria2_hook(pretrained_model_name_or_path: str, force_downloa
         with open(map_filename) as f:
             map_data = json.load(f)
         filenames = set(map_data["weight_map"].values())
-    urls = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=revision) for n in filenames]
+    urls = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=_revision) for n in filenames]
     if not force_download:
         urls = [u for u, n in zip(urls, filenames) if not is_cached(n)]
         if not urls:
@@ -485,6 +485,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
     import transformers
     import transformers.modeling_utils
     from huggingface_hub import HfFolder
+    _revision = koboldai_vars.revision if koboldai_vars.revision is not None else huggingface_hub.constants.DEFAULT_REVISION
     if shutil.which("aria2c") is None:  # Don't do anything if aria2 is not installed
         return
     if local_files_only:  # If local_files_only is true, we obviously don't need to download anything
@@ -519,7 +520,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
             filename = transformers.modeling_utils.WEIGHTS_INDEX_NAME if sharded else transformers.modeling_utils.WEIGHTS_NAME
         except AttributeError:
             return
-        url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=revision)
+        url = huggingface_hub.hf_hub_url(pretrained_model_name_or_path, filename, revision=_revision)
         if is_cached(url) or requests.head(url, allow_redirects=True, proxies=proxies, headers=headers):
             break
         if sharded:
@@ -533,7 +534,7 @@ def aria2_hook(pretrained_model_name_or_path: str, force_download=False, cache_d
         with open(map_filename) as f:
             map_data = json.load(f)
         filenames = set(map_data["weight_map"].values())
-    urls = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=revision) for n in filenames]
+    urls = [huggingface_hub.hf_hub_url(pretrained_model_name_or_path, n, revision=_revision) for n in filenames]
     if not force_download:
         urls = [u for u in urls if not is_cached(u)]
         if not urls:
@@ -580,7 +581,8 @@ def get_num_shards(filename):
 def get_sharded_checkpoint_num_tensors(pretrained_model_name_or_path, filename, cache_dir=None, force_download=False, proxies=None, resume_download=False, local_files_only=False, use_auth_token=None, user_agent=None, revision=None, **kwargs):
     import transformers.modeling_utils
     import torch
-    shard_paths, _ = transformers.modeling_utils.get_checkpoint_shard_files(pretrained_model_name_or_path, filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, revision=revision)
+    _revision = koboldai_vars.revision if koboldai_vars.revision is not None else huggingface_hub.constants.DEFAULT_REVISION
+    shard_paths, _ = transformers.modeling_utils.get_checkpoint_shard_files(pretrained_model_name_or_path, filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download, local_files_only=local_files_only, use_auth_token=use_auth_token, user_agent=user_agent, revision=_revision)
     return list(itertools.chain(*(torch.load(p, map_location="cpu").keys() for p in shard_paths)))
 
 #==================================================================#