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app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import pipeline
+import gradio as gr
+import time
+
+REPO_ID = "sayanbanerjee32/ms-phi2-qlora-oasst1"
+
+model = AutoModelForCausalLM.from_pretrained(REPO_ID)
+tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
+
+def generate_text(prompt, chat_history, num_new_tokens = 100):
+    # prompt = "<|prompter|>What is 2 + 2?<|endoftext|><|assistant|>"  # change to your desired prompt
+    
+    
+    input_prompt = ''
+    if len(chat_history) > 0:
+        input_prompt += "<|prompter|>" + chat_history[-1][0] + "<|endoftext|><|assistant|>" + chat_history[-1][1] + "<|endoftext|>"
+    input_prompt += "<|prompter|>" + prompt + "<|endoftext|><|assistant|>"
+    # Count the number of tokens in the prompt  
+    num_prompt_tokens = len(tokenizer(input_prompt)['input_ids'])
+    # Calculate the maximum length for the generation
+    max_length = num_prompt_tokens + num_new_tokens
+    gen = pipeline('text-generation', model=model,
+                tokenizer=tokenizer, max_length= max_length )
+    result = gen(prompt)
+    return result[0]['generated_text'].replace(prompt, '')
+
+with gr.Blocks() as demo:
+    gr.HTML("<h1 align = 'center'> Chat </h1>")
+    gr.HTML("<h4 align = 'center'> ChatBot powered by Microsoft-Phi-2 finetuned on OpenAssistant dataset</h4>")
+    
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    gr.Examples(["What do you think about ChatGPT?",
+                "How would the Future of AI in 10 Years look?",
+                "Write a announcement tweet for medium.com readers about the new blogpost on 'Open Assistant is open source ChatGPT that you don\'t wanna miss out'",
+                "Please implement the Timsort algorithm on Lean 4 and explain your code",
+                "How do I build a PC?"], 
+                inputs = msg)
+    clear = gr.ClearButton([msg, chatbot])
+
+    def respond(message, chat_history):
+        bot_message = generate_text(message, chat_history)
+        chat_history.append((message, bot_message))
+        time.sleep(2)
+        return "", chat_history
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+
+# # for collab
+# demo.launch(debug=True) 
+
+if __name__ == '__main__':
+    demo.launch() 

requirements.txt

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+torch
+accelerate
+transformers