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| import gradio as gr | |
| import random | |
| import time | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # Load Vicuna 7B model and tokenizer | |
| model_name = "lmsys/vicuna-7b-v1.3" | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# LLM Evaluator With Linguistic Scrutiny") | |
| gr.Markdown(" Description ") | |
| with gr.Tab("POS"): | |
| with gr.Row(): | |
| prompt = gr.Textbox(show_label=False, placeholder="Enter prompt") | |
| send_button_POS = gr.Button("Send", scale=0) | |
| gr.Markdown("Strategy 1 QA") | |
| with gr.Row(): | |
| vicuna_chatbot1 = gr.Chatbot(label="vicuna-7b", live=True) | |
| llama_chatbot1 = gr.Chatbot(label="llama-7b", live=False) | |
| gpt_chatbot1 = gr.Chatbot(label="gpt-3.5", live=False) | |
| gr.Markdown("Strategy 2 Instruction") | |
| with gr.Row(): | |
| vicuna_chatbot2 = gr.Chatbot(label="vicuna-7b", live=True) | |
| llama_chatbot2 = gr.Chatbot(label="llama-7b", live=False) | |
| gpt_chatbot2 = gr.Chatbot(label="gpt-3.5", live=False) | |
| gr.Markdown("Strategy 3 Structured Prompting") | |
| with gr.Row(): | |
| vicuna_chatbot3 = gr.Chatbot(label="vicuna-7b", live=True) | |
| llama_chatbot3 = gr.Chatbot(label="llama-7b", live=False) | |
| gpt_chatbot3 = gr.Chatbot(label="gpt-3.5", live=False) | |
| clear = gr.ClearButton([prompt, vicuna_chatbot1]) | |
| with gr.Tab("Chunk"): | |
| with gr.Row(): | |
| prompt_chunk = gr.Textbox(show_label=False, placeholder="Enter prompt") | |
| send_button_Chunk = gr.Button("Send", scale=0) | |
| gr.Markdown("Strategy 1 QA") | |
| with gr.Row(): | |
| vicuna_chatbot1_chunk = gr.Chatbot(label="vicuna-7b", live=True) | |
| llama_chatbot1_chunk = gr.Chatbot(label="llama-7b", live=False) | |
| gpt_chatbot1_chunk = gr.Chatbot(label="gpt-3.5", live=False) | |
| gr.Markdown("Strategy 2 Instruction") | |
| with gr.Row(): | |
| vicuna_chatbot2_chunk = gr.Chatbot(label="vicuna-7b", live=True) | |
| llama_chatbot2_chunk = gr.Chatbot(label="llama-7b", live=False) | |
| gpt_chatbot2_chunk = gr.Chatbot(label="gpt-3.5", live=False) | |
| gr.Markdown("Strategy 3 Structured Prompting") | |
| with gr.Row(): | |
| vicuna_chatbot3_chunk = gr.Chatbot(label="vicuna-7b", live=True) | |
| llama_chatbot3_chunk = gr.Chatbot(label="llama-7b", live=False) | |
| gpt_chatbot3_chunk = gr.Chatbot(label="gpt-3.5", live=False) | |
| clear = gr.ClearButton([prompt_chunk, vicuna_chatbot1_chunk]) | |
| # Define the function for generating responses | |
| def generate_response(model, tokenizer, prompt): | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| outputs = model.generate(**inputs, max_length=500, pad_token_id=tokenizer.eos_token_id) | |
| response = tokenizer.decode(outputs[0]) | |
| return response | |
| # Define the Gradio interface | |
| def chatbot_interface(prompt): | |
| vicuna_response = generate_response(model, tokenizer, prompt) | |
| # llama_response = generate_response(llama_model, llama_tokenizer, prompt) | |
| return {"Vicuna-7B": vicuna_response} | |
| # Replace the old respond function with the new general function for Vicuna | |
| prompt.submit(chatbot_interface, [prompt, vicuna_chatbot1, vicuna_chatbot1_chunk]) | |
| demo.launch() | |