diff --git a/convert-to-torch.py b/convert-to-torch.py
deleted file mode 120000
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--- a/convert-to-torch.py
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-../convert-to-torch.py
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diff --git a/server.py b/server.py
deleted file mode 120000
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--- a/server.py
+++ /dev/null
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-../server.py
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diff --git a/server.py b/server.py
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--- /dev/null
+++ b/server.py
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+import time
+import re
+import torch
+import gradio as gr
+import transformers
+from transformers import AutoTokenizer
+from transformers import GPTJForCausalLM, AutoModelForCausalLM, AutoModelForSeq2SeqLM, OPTForCausalLM, T5Tokenizer, T5ForConditionalGeneration, GPTJModel, AutoModel
+
+#model_name = "bloomz-7b1-p3"
+#model_name = 'gpt-j-6B-float16'
+#model_name = "opt-6.7b"
+#model_name = 'opt-13b'
+#model_name = "gpt4chan_model_float16"
+model_name = 'galactica-6.7b'
+#model_name = 'gpt-neox-20b'
+#model_name = 'flan-t5'
+#model_name = 'OPT-13B-Erebus'
+
+def load_model(model_name):
+    print(f"Loading {model_name}")
+
+    t0 = time.time()
+    if model_name in ['gpt-neox-20b', 'opt-13b', 'OPT-13B-Erebus']:
+        model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", device_map='auto', load_in_8bit=True)
+    elif model_name in ['gpt-j-6B']:
+        model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
+    elif model_name in ['flan-t5']:
+        model = T5ForConditionalGeneration.from_pretrained(f"models/{model_name}").cuda()
+    else:
+        model = torch.load(f"torch-dumps/{model_name}.pt").cuda()
+
+    if model_name in ['gpt4chan_model_float16']:
+        tokenizer = AutoTokenizer.from_pretrained("models/gpt-j-6B/")
+    elif model_name in ['flan-t5']:
+        tokenizer = T5Tokenizer.from_pretrained(f"models/{model_name}/")
+    else:
+        tokenizer = AutoTokenizer.from_pretrained(f"models/{model_name}/")
+
+    print(f"Loaded the model in {time.time()-t0} seconds.")
+    return model, tokenizer
+
+def fix_gpt4chan(s):
+    for i in range(10):
+        s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
+        s = re.sub("--- [0-9]*\n *\n---", "---", s)
+        s = re.sub("--- [0-9]*\n\n\n---", "---", s)
+
+    return s
+
+def fn(question, temperature, max_length, inference_settings, selected_model):
+    global model, tokenizer, model_name
+
+    if selected_model != model_name:
+        model_name = selected_model
+        model = None
+        tokenier = None
+        torch.cuda.empty_cache()
+        model, tokenizer = load_model(model_name)
+
+    torch.cuda.empty_cache()
+    input_text = question
+    input_ids = tokenizer.encode(str(input_text), return_tensors='pt').cuda()
+
+    if inference_settings == 'Default':
+        output = model.generate(
+            input_ids,
+            do_sample=True,
+            max_new_tokens=max_length,
+            #max_length=max_length+len(input_ids[0]),
+            top_p=1,
+            typical_p=0.3,
+            temperature=temperature, 
+        ).cuda()
+    elif inference_settings == 'Verbose':
+        output = model.generate(
+            input_ids,
+            num_beams=10,
+            min_length=max_length,
+            max_new_tokens=max_length,
+            length_penalty =1.4,
+            no_repeat_ngram_size=2,
+            early_stopping=True,
+            temperature=0.7,
+            top_k=150,
+            top_p=0.92,
+            repetition_penalty=4.5,
+        ).cuda()
+
+    reply = tokenizer.decode(output[0], skip_special_tokens=True)
+    if model_name.startswith('gpt4chan'):
+        reply = fix_gpt4chan(reply)
+
+    return reply
+
+model, tokenizer = load_model(model_name)
+if model_name.startswith('gpt4chan'):
+    default_text = "-----\n--- 865467536\nInput text\n--- 865467537\n"
+else:
+    default_text = "Common sense questions and answers\n\nQuestion: \nFactual answer:"
+
+interface = gr.Interface(
+    fn,
+    inputs=[
+        gr.Textbox(value=default_text, lines=15),
+        gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7),
+        gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200),
+        gr.Dropdown(choices=["Default", "Verbose"], value="Default"),
+        gr.Dropdown(choices=["gpt4chan_model_float16", "galactica-6.7b", "opt-6.7b",  "opt-13b", "gpt-neox-20b", "gpt-j-6B-float16", "flan-t5", "bloomz-7b1-p3", "OPT-13B-Erebus"], value=model_name),
+    ],
+    outputs=[
+         gr.Textbox(placeholder="", lines=15),
+    ],
+    title="Text generation lab",
+    description=f"Generate text using Large Language Models. Currently working with {model_name}",
+)
+
+interface.launch(share=False, server_name="0.0.0.0")