Finally it works

app.py

@@ -13,9 +13,54 @@ subprocess.run(
     env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
     shell=True,
 )
+
+theme = gr.themes.Base(
+    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
+)
+
+model_name = "microsoft/Phi-3-medium-4k-instruct"
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map='cuda', torch_dtype=torch.float16, _attn_implementation="flash_attention_2", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = [29, 0]
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+
+@spaces.GPU(queue=False)
+def predict1(message, history, temperature1, max_tokens1, repetition_penalty1, top_p1):
+    history_transformer_format = history + [[message, ""]]
+    stop = StopOnTokens()
+    messages = "".join(["".join(["\n<|end|>\n<|user|>\n"+item[0], "\n<|end|>\n<|assistant|>\n"+item[1]]) for item in history_transformer_format])
+    model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
+    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=max_tokens1,
+        do_sample=True,
+        top_p=top_p1,
+        repetition_penalty=repetition_penalty1,
+        temperature=temperature1,
+        stopping_criteria=StoppingCriteriaList([stop])
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    partial_message = ""
+    for new_token in streamer:
+        if new_token != '<':
+            partial_message += new_token
+        yield partial_message
+
+
+
 model_name = "microsoft/Phi-3-medium-128k-instruct"
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model = AutoModelForCausalLM.from_pretrained(model_name, device_map='cuda', _attn_implementation="flash_attention_2", torch_dtype=torch.float16, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map='cuda', torch_dtype=torch.float16, _attn_implementation="flash_attention_2", trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 class StopOnTokens(StoppingCriteria):
@@ -25,9 +70,9 @@ class StopOnTokens(StoppingCriteria):
             if input_ids[0][-1] == stop_id:
                 return True
         return False
-model.to('cuda')
+
 @spaces.GPU(queue=False)
-def predict(message, history, temperature, max_tokens, top_p, top_k):
+def predict(message, history, temperature, max_tokens, repetition_penalty, top_p):
     history_transformer_format = history + [[message, ""]]
     stop = StopOnTokens()
     messages = "".join(["".join(["\n<|end|>\n<|user|>\n"+item[0], "\n<|end|>\n<|assistant|>\n"+item[1]]) for item in history_transformer_format])
@@ -39,7 +84,7 @@ def predict(message, history, temperature, max_tokens, top_p, top_k):
         max_new_tokens=max_tokens,
         do_sample=True,
         top_p=top_p,
-        top_k=top_k,
+        repetition_penalty=repetition_penalty,
         temperature=temperature,
         stopping_criteria=StoppingCriteriaList([stop])
     )
@@ -51,14 +96,120 @@ def predict(message, history, temperature, max_tokens, top_p, top_k):
             partial_message += new_token
         yield partial_message
 
-demo = gr.ChatInterface(
-    fn=predict,
-    title="Phi-3-medium-128k-instruct",
+max_tokens1 = gr.Slider(
+    minimum=512,
+    maximum=4096,
+    value=4000,
+    step=32,
+    interactive=True,
+    label="Maximum number of new tokens to generate",
+)
+repetition_penalty1 = gr.Slider(
+    minimum=0.01,
+    maximum=5.0,
+    value=1,
+    step=0.01,
+    interactive=True,
+    label="Repetition penalty",
+)
+temperature1 = gr.Slider(
+    minimum=0.0,
+    maximum=1.0,
+    value=0.7,
+    step=0.05,
+    visible=True,
+    interactive=True,
+    label="Temperature",
+)
+top_p1 = gr.Slider(
+    minimum=0.01,
+    maximum=0.99,
+    value=0.9,
+    step=0.01,
+    visible=True,
+    interactive=True,
+    label="Top P",
+)
+
+chatbot1 = gr.Chatbot(
+    label="Phi3-medium-4k",
+    show_copy_button=True, 
+    likeable=True, 
+    layout="panel"
+)
+
+output=gr.Textbox(label="Prompt")
+
+with gr.Blocks() as min:  
+    gr.ChatInterface(
+    fn=predict1,
+    chatbot=chatbot1,
     additional_inputs=[
-        gr.Slider(0.1, 0.9, step=0.1, value=0.7, label="Temperature"),
-        gr.Slider(512, 8192, value=4096, label="Max Tokens"),
-        gr.Slider(0.1, 0.9, step=0.1, value=0.7, label="top_p"),
-        gr.Slider(10, 90, step=10, value=40, label="top_k"),
-    ]
+        temperature1,
+        max_tokens1,
+        repetition_penalty1,
+        top_p1,
+        ],   
+    )
+
+max_tokens = gr.Slider(
+    minimum=64000,
+    maximum=128000,
+    value=100000,
+    step=1000,
+    interactive=True,
+    label="Maximum number of new tokens to generate",
+)
+repetition_penalty = gr.Slider(
+    minimum=0.01,
+    maximum=5.0,
+    value=1,
+    step=0.01,
+    interactive=True,
+    label="Repetition penalty",
+)
+temperature = gr.Slider(
+    minimum=0.0,
+    maximum=1.0,
+    value=0.7,
+    step=0.05,
+    visible=True,
+    interactive=True,
+    label="Temperature",
+)
+top_p = gr.Slider(
+    minimum=0.01,
+    maximum=0.99,
+    value=0.9,
+    step=0.01,
+    visible=True,
+    interactive=True,
+    label="Top P",
 )
+
+chatbot = gr.Chatbot(
+    label="Phi3-medium-128k",
+    show_copy_button=True, 
+    likeable=True, 
+    layout="panel"
+)
+
+output=gr.Textbox(label="Prompt")
+    
+with gr.Blocks() as max:
+    gr.ChatInterface(
+    fn=predict,
+    chatbot=chatbot,
+    additional_inputs=[
+        temperature,
+        max_tokens,
+        repetition_penalty,
+        top_p,
+        ],   
+    )
+
+with gr.Blocks(title="Phi 3 Medium DEMO", theme=theme) as demo:
+    gr.Markdown("# Phi3 Medium all in one")
+    gr.TabbedInterface([max, min], ['Phi3 medium 128k','Phi3 medium 4k'])
+        
 demo.launch(share=True)