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import os |
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from threading import Thread |
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from typing import Iterator |
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import gradio as gr |
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import torch |
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import spaces |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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MODEL_LIST = ["LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"] |
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HF_TOKEN = os.environ.get("HF_TOKEN", None) |
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MODEL = os.environ.get("MODEL_ID") |
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DESCRIPTION = """\ |
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# EXAONE 3.0 7.8B Instruct |
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##### We hope EXAONE continues to advance Expert AI with its effectiveness and bilingual skills. |
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<center>This is a official demo of <a href=https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct>LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct</a>, fine-tuned for instruction following.</center> |
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<center>π For more details, please check <a href=https://www.lgresearch.ai/blog/view?seq=460>our blog</a> or <a href=https://arxiv.org/abs/2408.03541>technical report</a></center> |
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""" |
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MAX_MAX_NEW_TOKENS = 4096 |
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DEFAULT_MAX_NEW_TOKENS = 128 |
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "3840")) |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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tokenizer = AutoTokenizer.from_pretrained(MODEL) |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL, |
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torch_dtype=torch.bfloat16, |
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trust_remote_code=True, |
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device_map="auto", |
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) |
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model.eval() |
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@spaces.GPU() |
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def generate( |
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message: str, |
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chat_history: list[tuple[str, str]], |
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system_prompt: str, |
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max_new_tokens: int = 128, |
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temperature: float = 0.6, |
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top_p: float = 0.9, |
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top_k: int = 50, |
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) -> Iterator[str]: |
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messages = [{"role":"system","content": system_prompt}] |
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print(f'message: {message}') |
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print(f'chat_history: {chat_history}') |
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for user, assistant in chat_history: |
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messages.extend( |
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[ |
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{"role": "user", "content": user}, |
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{"role": "assistant", "content": assistant}, |
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] |
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) |
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messages.append({"role": "user", "content": message}) |
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input_ids = tokenizer.apply_chat_template( |
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messages, |
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add_generation_prompt=True, |
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return_tensors="pt" |
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) |
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if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: |
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input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] |
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gr.Warning(f"Trimmed input from messages as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") |
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input_ids = input_ids.to(model.device) |
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streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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{"input_ids": input_ids}, |
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streamer=streamer, |
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max_new_tokens=max_new_tokens, |
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do_sample=False if top_k == 1 else True, |
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top_p=top_p, |
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top_k=top_k, |
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temperature=temperature, |
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num_beams=1, |
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repetition_penalty=1.0, |
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) |
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t = Thread(target=model.generate, kwargs=generate_kwargs) |
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t.start() |
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outputs = [] |
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for text in streamer: |
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outputs.append(text) |
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yield "".join(outputs) |
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BOT_AVATAR = "EXAONE_logo.png" |
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chatbot = gr.Chatbot( |
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label="EXAONE-3.0-7.8B-Instruct", |
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avatar_images=[None, BOT_AVATAR], |
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layout="bubble", |
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bubble_full_width=False |
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) |
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chat_interface = gr.ChatInterface( |
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fn=generate, |
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chatbot=chatbot, |
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additional_inputs=[ |
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gr.Textbox( |
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value="You are EXAONE model from LG AI Research, a helpful assistant.", |
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label="System Prompt", |
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render=False, |
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), |
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gr.Slider( |
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label="Max new tokens", |
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minimum=1, |
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maximum=MAX_MAX_NEW_TOKENS, |
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step=1, |
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value=DEFAULT_MAX_NEW_TOKENS, |
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), |
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gr.Slider( |
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label="Temperature", |
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minimum=0.1, |
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maximum=2.0, |
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step=0.1, |
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value=0.7, |
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), |
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gr.Slider( |
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label="Top-p (nucleus sampling)", |
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minimum=0.05, |
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maximum=1.0, |
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step=0.05, |
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value=0.9, |
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), |
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gr.Slider( |
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label="Top-k", |
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minimum=1, |
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maximum=1000, |
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step=1, |
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value=50, |
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), |
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], |
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stop_btn=None, |
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examples=[ |
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["Explain who you are"], |
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["λμ μμμ λ§ν΄λ΄"], |
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], |
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cache_examples=False, |
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) |
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with gr.Blocks(css="style.css", fill_height=True) as demo: |
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gr.Markdown("""<p align="center"><img src="./EXAONE_logo.png" style="height: 80px"/><p>""") |
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gr.Markdown(DESCRIPTION) |
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chat_interface.render() |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |