import spaces from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer import torch import gradio as gr import logging from huggingface_hub import login import os from threading import Thread # Status: Breaks during generation logging.basicConfig(level=logging.DEBUG) HF_TOKEN = os.environ.get("HF_TOKEN", None) login(token=HF_TOKEN) models_available = [ "NousResearch/Meta-Llama-3.1-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.3", ] tokenizer_a, model_a = None, None tokenizer_b, model_b = None, None torch_dtype = torch.bfloat16 def apply_chat_template(messages, add_generation_prompt=False): """ Function to apply the chat template manually for each message in a list. messages: List of dictionaries, each containing a 'role' and 'content'. """ pharia_template = """<|begin_of_text|>""" role_map = { "system": "<|start_header_id|>system<|end_header_id|>\n", "user": "<|start_header_id|>user<|end_header_id|>\n", "assistant": "<|start_header_id|>assistant<|end_header_id|>\n", } # Iterate through the messages and apply the template for each role for message in messages: role = message["role"] content = message["content"] pharia_template += role_map.get(role, "") + content + "<|eot_id|>\n" # Add the assistant generation prompt if required if add_generation_prompt: pharia_template += "<|start_header_id|>assistant<|end_header_id|>\n" return pharia_template def load_model_a(model_id): global tokenizer_a, model_a, model_id_a model_id_a = model_id # need to access model_id with tokenizer tokenizer_a = AutoTokenizer.from_pretrained(model_id) logging.debug(f"***** model A eos_token: {tokenizer_a.eos_token}") model_a = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch_dtype, device_map="auto", trust_remote_code=True, ).eval() return gr.update(label=model_id) def load_model_b(model_id): global tokenizer_b, model_b, model_id_b model_id_b = model_id tokenizer_b = AutoTokenizer.from_pretrained(model_id) logging.debug(f"***** model B eos_token: {tokenizer_b.eos_token}") model_b = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch_dtype, device_map="auto", trust_remote_code=True, ).eval() model_b.tie_weights() return gr.update(label=model_id) @spaces.GPU() def generate_both(system_prompt, input_text, chatbot_a, chatbot_b, max_new_tokens=2048, temperature=0.2, top_p=0.9, repetition_penalty=1.1): text_streamer_a = TextIteratorStreamer(tokenizer_a, skip_prompt=True) text_streamer_b = TextIteratorStreamer(tokenizer_b, skip_prompt=True) system_prompt_list = [{"role": "system", "content": system_prompt}] if system_prompt else [] input_text_list = [{"role": "user", "content": input_text}] chat_history_a = [] for user, assistant in chatbot_a: chat_history_a.append({"role": "user", "content": user}) chat_history_a.append({"role": "assistant", "content": assistant}) chat_history_b = [] for user, assistant in chatbot_b: chat_history_b.append({"role": "user", "content": user}) chat_history_b.append({"role": "assistant", "content": assistant}) new_messages_a = system_prompt_list + chat_history_a + input_text_list new_messages_b = system_prompt_list + chat_history_b + input_text_list input_ids_a = tokenizer_a.apply_chat_template( new_messages_a, add_generation_prompt=True, return_tensors="pt" ).to(model_a.device) input_ids_b = tokenizer_b.apply_chat_template( new_messages_b, add_generation_prompt=True, return_tensors="pt" ).to(model_b.device) generation_kwargs_a = dict( input_ids=input_ids_a, streamer=text_streamer_a, max_new_tokens=max_new_tokens, pad_token_id=tokenizer_a.eos_token_id, do_sample=True, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty, ) generation_kwargs_b = dict( input_ids=input_ids_b, streamer=text_streamer_b, max_new_tokens=max_new_tokens, pad_token_id=tokenizer_b.eos_token_id, do_sample=True, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty, ) thread_a = Thread(target=model_a.generate, kwargs=generation_kwargs_a) thread_b = Thread(target=model_b.generate, kwargs=generation_kwargs_b) thread_a.start() thread_b.start() chatbot_a.append([input_text, ""]) chatbot_b.append([input_text, ""]) finished_a = False finished_b = False while not (finished_a and finished_b): if not finished_a: try: text_a = next(text_streamer_a) if tokenizer_a.eos_token in text_a: eot_location = text_a.find(tokenizer_a.eos_token) text_a = text_a[:eot_location] finished_a = True chatbot_a[-1][-1] += text_a yield chatbot_a, chatbot_b except StopIteration: finished_a = True if not finished_b: try: text_b = next(text_streamer_b) if tokenizer_b.eos_token in text_b: eot_location = text_b.find(tokenizer_b.eos_token) text_b = text_b[:eot_location] finished_b = True chatbot_b[-1][-1] += text_b yield chatbot_a, chatbot_b except StopIteration: finished_b = True return chatbot_a, chatbot_b def clear(): return [], [] arena_notes = """## Important Notes: - Sometimes an error may occur when generating the response, in this case, please try again. """ with gr.Blocks() as demo: with gr.Column(): gr.HTML("

🤖le Royale

") gr.Markdown(arena_notes) system_prompt = gr.Textbox(lines=1, label="System Prompt", value="You are a helpful chatbot. Write a Nike style ad headline about the shame of being second best", show_copy_button=True) with gr.Row(variant="panel"): with gr.Column(): model_dropdown_a = gr.Dropdown(label="Model A", choices=models_available, value=None) chatbot_a = gr.Chatbot(label="Model A", rtl=True, likeable=True, show_copy_button=True, height=500) with gr.Column(): model_dropdown_b = gr.Dropdown(label="Model B", choices=models_available, value=None) chatbot_b = gr.Chatbot(label="Model B", rtl=True, likeable=True, show_copy_button=True, height=500) with gr.Row(variant="panel"): with gr.Column(scale=1): submit_btn = gr.Button(value="Generate", variant="primary") clear_btn = gr.Button(value="Clear", variant="secondary") input_text = gr.Textbox(lines=1, label="Output", value="", scale=3, show_copy_button=True) with gr.Accordion(label="Generation Configurations", open=False): max_new_tokens = gr.Slider(minimum=128, maximum=4096, value=2048, label="Max New Tokens", step=128) temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, label="Temperature", step=0.01) top_p = gr.Slider(minimum=0.0, maximum=1.0, value=1.0, label="Top-p", step=0.01) repetition_penalty = gr.Slider(minimum=0.1, maximum=2.0, value=1.1, label="Repetition Penalty", step=0.1) model_dropdown_a.change(load_model_a, inputs=[model_dropdown_a], outputs=[chatbot_a]) model_dropdown_b.change(load_model_b, inputs=[model_dropdown_b], outputs=[chatbot_b]) input_text.submit(generate_both, inputs=[system_prompt, input_text, chatbot_a, chatbot_b, max_new_tokens, temperature, top_p, repetition_penalty], outputs=[chatbot_a, chatbot_b]) submit_btn.click(generate_both, inputs=[system_prompt, input_text, chatbot_a, chatbot_b, max_new_tokens, temperature, top_p, repetition_penalty], outputs=[chatbot_a, chatbot_b]) clear_btn.click(clear, outputs=[chatbot_a, chatbot_b]) if __name__ == "__main__": demo.queue().launch()