Remove T5 support (it sucks)

server.py

@@ -8,8 +8,7 @@ from pathlib import Path
 import gradio as gr
 import transformers
 from html_generator import *
-from transformers import AutoTokenizer, T5Tokenizer
-from transformers import AutoModelForCausalLM, T5ForConditionalGeneration
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
 
 parser = argparse.ArgumentParser()
@@ -37,8 +36,6 @@ def load_model(model_name):
             model = torch.load(Path(f"torch-dumps/{model_name}.pt"))
         elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')) and any(size in model_name.lower() for size in ('13b', '20b', '30b')):
             model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), device_map='auto', load_in_8bit=True)
-        elif model_name in ['flan-t5', 't5-large']:
-            model = T5ForConditionalGeneration.from_pretrained(Path(f"models/{model_name}")).cuda()
         else:
             model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
 
@@ -46,11 +43,7 @@ def load_model(model_name):
     else:
         settings = ["low_cpu_mem_usage=True"]
         cuda = ""
-
-        if model_name in ['flan-t5', 't5-large']:
-            command = f"T5ForConditionalGeneration.from_pretrained"
-        else:
-            command = "AutoModelForCausalLM.from_pretrained"
+        command = "AutoModelForCausalLM.from_pretrained"
 
         if args.cpu:
             settings.append("torch_dtype=torch.float32")
@@ -72,8 +65,6 @@ def load_model(model_name):
     # Loading the tokenizer
     if model_name.lower().startswith('gpt4chan') and Path(f"models/gpt-j-6B/").exists():
         tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
-    elif model_name in ['flan-t5', 't5-large']:
-        tokenizer = T5Tokenizer.from_pretrained(Path(f"models/{model_name}/"))
     else:
         tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{model_name}/"))