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| from threading import Thread # Import the Thread class from the threading module | |
| import torch # Import the PyTorch library | |
| import gradio as gr # Import Gradio for creating a UI | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TextIteratorStreamer # Import Hugging Face Transformers | |
| # Define the Hugging Face model ID and check for available GPU (cuda) | |
| model_id = "declare-lab/flan-alpaca-large" | |
| torch_device = "cuda" if torch.cuda.is_available() else "cpu" | |
| print("Running on device:", torch_device) | |
| print("CPU threads:", torch.get_num_threads()) | |
| # Load the pre-trained model based on the device | |
| if torch_device == "cuda": | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_id, load_in_8bit=True, device_map="auto") | |
| else: | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_id) | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| # Define a function to run model text generation | |
| def run_generation(user_text, top_p, temperature, top_k, max_new_tokens): | |
| # Get the model and tokenizer, and tokenize the user text. | |
| model_inputs = tokenizer([user_text], return_tensors="pt").to(torch_device) | |
| # Start generation on a separate thread, so that we don't block the UI. | |
| # Adds timeout to the streamer to handle exceptions in the generation thread. | |
| streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True) | |
| generate_kwargs = dict( | |
| model_inputs, | |
| streamer=streamer, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=True, | |
| top_p=top_p, | |
| temperature=float(temperature), | |
| top_k=top_k | |
| ) | |
| # Create a new thread for model generation | |
| t = Thread(target=model.generate, kwargs=generate_kwargs) | |
| t.start() | |
| model_output = "" | |
| for new_text in streamer: | |
| model_output += new_text | |
| yield model_output | |
| return model_output | |
| # Define a function to reset the user input textbox | |
| def reset_textbox(): | |
| return gr.update(value='') | |
| # Create a Gradio UI interface | |
| with gr.Blocks() as demo: | |
| # Display a title | |
| gr.Markdown( | |
| "# Testing ALPACA Model \n" | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=4): | |
| # Create a textbox for user input | |
| user_text = gr.Textbox( | |
| placeholder="Ask Me Anything ... ", | |
| label="User input" | |
| ) | |
| # Create a textbox for model output | |
| model_output = gr.Textbox(label="Model output", lines=10, interactive=False) | |
| # Create a submit button | |
| button_submit = gr.Button(value="Submit") | |
| with gr.Column(scale=1): | |
| # Create sliders for adjusting generation parameters | |
| max_new_tokens = gr.Slider( | |
| minimum=1, maximum=1000, value=250, step=1, interactive=True, label="Max New Tokens", | |
| ) | |
| top_p = gr.Slider( | |
| minimum=0.05, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p (nucleus sampling)", | |
| ) | |
| top_k = gr.Slider( | |
| minimum=1, maximum=50, value=50, step=1, interactive=True, label="Top-k", | |
| ) | |
| temperature = gr.Slider( | |
| minimum=0.1, maximum=5.0, value=0.8, step=0.1, interactive=True, label="Temperature", | |
| ) | |
| # Set up the submission of user input | |
| user_text.submit(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output) | |
| button_submit.click(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output) | |
| # Launch the Gradio interface | |
| demo.queue(max_size=32).launch(enable_queue=True) |