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| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer, pipeline | |
| from threading import Thread | |
| # The huggingface model id for Microsoft's phi-2 model | |
| checkpoint = "microsoft/phi-2" | |
| # Download and load model and tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.float32, device_map="cpu", trust_remote_code=True) | |
| # Text generation pipeline | |
| phi2 = pipeline( | |
| "text-generation", | |
| tokenizer=tokenizer, | |
| model=model, | |
| pad_token_id=tokenizer.eos_token_id, | |
| eos_token_id=tokenizer.eos_token_id, | |
| device_map="cpu" | |
| ) | |
| # Function that accepts a prompt and generates text using the phi2 pipeline | |
| def generate(message, chat_history, max_new_tokens): | |
| instruction = "You are a helpful assistant to 'User'. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'." | |
| final_prompt = f"Instruction: {instruction}\n" | |
| for sent, received in chat_history: | |
| final_prompt += "User: " + sent + "\n" | |
| final_prompt += "Assistant: " + received + "\n" | |
| final_prompt += "User: " + message + "\n" | |
| final_prompt += "Output:" | |
| if len(tokenizer.tokenize(final_prompt)) >= tokenizer.model_max_length - max_new_tokens: | |
| final_prompt = "Instruction: Say 'Input exceeded context size, please clear the chat history and retry!' Output:" | |
| # Streamer | |
| streamer = TextIteratorStreamer(tokenizer=tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=300.0) | |
| thread = Thread(target=phi2, kwargs={"text_inputs":final_prompt, "max_new_tokens":max_new_tokens, "streamer":streamer}) | |
| thread.start() | |
| generated_text = "" | |
| for word in streamer: | |
| generated_text += word | |
| response = generated_text.strip() | |
| if "User:" in response: | |
| response = response.split("User:")[0].strip() | |
| if "Assistant:" in response: | |
| response = response.split("Assistant:")[1].strip() | |
| yield response | |
| # Chat interface with gradio | |
| with gr.Blocks() as demo: | |
| gr.Markdown(""" | |
| # Phi-2 Chatbot Demo | |
| This chatbot was created using Microsoft's 2.7 billion parameter [phi-2](https://huggingface.co/microsoft/phi-2) Transformer model. | |
| In order to reduce the response time on this hardware, `max_new_tokens` has been set to `21` in the text generation pipeline. With this default configuration, it takes approximately `60 seconds` for the response to start being generated, and streamed one word at a time. Use the slider below to increase or decrease the length of the generated text. | |
| """) | |
| tokens_slider = gr.Slider(8, 128, value=21, label="Maximum new tokens", info="A larger `max_new_tokens` parameter value gives you longer text responses but at the cost of a slower response time.") | |
| chatbot = gr.ChatInterface( | |
| fn=generate, | |
| additional_inputs=[tokens_slider], | |
| stop_btn=None, | |
| examples=[["Who is Leonhard Euler?"]] | |
| ) | |
| demo.queue().launch() |