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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). | |
| 🔎 For more details about the finetuning, take a look at the [GitHub](https://github.com/mkthoma/llm_finetuning) code. | |
| """ | |
| 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=["/content/logo/user logo.png", "/content/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=2048,step=50,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) |