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| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer, pipeline | |
| from threading import Thread | |
| import gradio as gr | |
| DEVICE = "cpu" | |
| if torch.cuda.is_available(): | |
| DEVICE = "cuda" | |
| # The huggingface model id for phi-2 instruct model | |
| checkpoint = "rasyosef/phi-2-instruct-v0.1" | |
| # Download and load model and tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(checkpoint) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| checkpoint, | |
| torch_dtype=torch.float32, | |
| device_map=DEVICE | |
| ) | |
| # 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=DEVICE | |
| ) | |
| # Function that accepts a prompt and generates text using the phi2 pipeline | |
| def generate(message, chat_history, max_new_tokens=64): | |
| history = [ | |
| {"role": "system", "content": "You are Phi, a helpful AI assistant made by Microsoft and RasYosef. User will you give you a task. Your goal is to complete the task as faithfully as you can."} | |
| ] | |
| for sent, received in chat_history: | |
| history.append({"role": "user", "content": sent}) | |
| history.append({"role": "assistant", "content": received}) | |
| history.append({"role": "user", "content": message}) | |
| #print(history) | |
| if len(tokenizer.apply_chat_template(history)) > 512: | |
| yield "chat history is too long" | |
| else: | |
| # Streamer | |
| streamer = TextIteratorStreamer(tokenizer=tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=300.0) | |
| thread = Thread(target=phi2, kwargs={"text_inputs":history, "max_new_tokens":max_new_tokens, "streamer":streamer}) | |
| thread.start() | |
| generated_text = "" | |
| for word in streamer: | |
| generated_text += word | |
| response = generated_text.strip() | |
| yield response | |
| # Chat interface with gradio | |
| with gr.Blocks() as demo: | |
| gr.Markdown(""" | |
| # Phi-2 Chatbot Demo | |
| This chatbot was created using a finetuned version of Microsoft's 2.7 billion parameter Phi 2 transformer model, [Phi-2-Instruct-v0.1](https://huggingface.co/rasyosef/Phi-1_5-Instruct-v0.1) that has underwent a post-training process that incorporates both **supervised fine-tuning** and **direct preference optimization** for instruction following. | |
| """) | |
| tokens_slider = gr.Slider(8, 256, value=64, 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( | |
| chatbot=gr.Chatbot(height=400), | |
| fn=generate, | |
| additional_inputs=[tokens_slider], | |
| stop_btn=None, | |
| examples=[ | |
| ["Hi"], | |
| ["What's the German word for 'car'?"], | |
| ["Molly and Abigail want to attend a beauty and modeling contest. They both want to buy new pairs of shoes and dresses. Molly buys a pair of shoes which costs $40 and a dress which costs $160. How much should Abigail budget if she wants to spend half of what Molly spent on the pair of shoes and dress?"], | |
| ] | |
| ) | |
| demo.queue().launch(debug=True) |