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app.py

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+import gradio as gr
+from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = "finetuned_phi2"
+model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+num_new_tokens = 200  # change to the number of new tokens you want to generate
+
+DESCRIPTION = """\
+# 🧑🏽‍💻Microsoft Phi2 Chatbot🤖
+This Space demonstrates model [Microsoft Phi2 2.7B](https://huggingface.co/microsoft/phi-2), a model with 2.78B parameters fine-tuned for chat instructions. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints).
+\n 🔎 For more details about the finetuning, take a look at the [GitHub](https://github.com/mkthoma/llm_finetuning) code.
+\n ⛔⛔ The model is hosted on a CPU and inference takes a long time. Please feel free to duplicate the space and use it on a GPU ⛔⛔
+"""
+
+LICENSE = """
+As a derivate work of [Microsoft Phi2 2.7B](https://huggingface.co/microsoft/phi-2), this demo is governed by the original [license](https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE).
+"""
+
+def generate(question, context, max_new_tokens = 200, temperature = 0.6):
+  
+    system_message = "You are a question answering chatbot. Provide a clear and detailed explanation"
+    prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n {question} [/INST]" # replace the command here with something relevant to your task
+
+    # Count the number of tokens in the prompt
+    num_prompt_tokens = len(tokenizer(prompt)['input_ids'])
+    # Calculate the maximum length for the generation
+    max_length = num_prompt_tokens + max_new_tokens
+
+    gen = pipeline('text-generation', model=model, tokenizer=tokenizer, max_length=max_length, temperature=temperature)
+    result = gen(prompt)
+    return (result[0]['generated_text'].replace(prompt, ''))
+
+
+bbchatbot = gr.Chatbot(
+    avatar_images=["logo/user logo.png", "logo/bot logo.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
+
+examples = [["What is a large language model?"], ["How to calm down a person?"], ["What is aritificial intelligence?"], ["How to write a good resume?"]]
+
+additional_inputs =  additional_inputs=[gr.Slider(label="Max new tokens",minimum=100,maximum=500,step=10,value=num_new_tokens),
+                                        gr.Slider(label="Temperature",minimum=0.1,maximum=4.0,step=0.1,value=0.6)]
+
+chat_interface  = gr.ChatInterface(fn=generate,
+                        additional_inputs=additional_inputs,
+                        chatbot=bbchatbot,
+                        title="",
+                        examples=examples
+                       )
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(value="Duplicate for private use", elem_id="duplicate-button")
+    chat_interface.render()
+    gr.Markdown(LICENSE)
+
+demo.queue().launch(show_api=False)

requirements.txt

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+accelerate
+peft
+bitsandbytes
+transformers
+trl
+einops
+huggingface_hub
+gradio
+torch