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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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import gradio as gr |
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from threading import Thread |
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model_id = "ibm-granite/granite-3.0-3b-a800m-instruct" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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device_map="cpu", |
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) |
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TITLE = "<h1><center>ibm-granite/granite-3.0-3b-a800m-instruct by CPU</center></h1>" |
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DESCRIPTION = """ |
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<h3>MODEL: <a href="https://huggingface.co/ibm-granite/granite-3.0-3b-a800m-instruct">ibm-granite/granite-3.0-3b-a800m-instruct</a></h3> |
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<center> |
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<p>This model is designed for conversational interactions.</p> |
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</center> |
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""" |
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CSS = """ |
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.duplicate-button { |
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margin: auto !important; |
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color: white !important; |
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background: black !important; |
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border-radius: 100vh !important; |
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} |
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h3 { |
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text-align: center; |
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} |
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.chatbox .messages .message.user { |
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background-color: #e1f5fe; |
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} |
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.chatbox .messages .message.bot { |
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background-color: #eeeeee; |
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} |
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""" |
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def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float): |
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print(f'Message: {message}') |
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print(f'History: {history}') |
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conversation = [] |
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for prompt, answer in history: |
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conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}]) |
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conversation.append({"role": "user", "content": message}) |
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input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(model.device) |
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streamer = TextIteratorStreamer(tokenizer, timeout=10., 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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top_k=top_k, |
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top_p=top_p, |
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repetition_penalty=penalty, |
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max_new_tokens=max_new_tokens, |
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do_sample=True, |
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temperature=temperature, |
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) |
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thread = Thread(target=model.generate, kwargs=generate_kwargs) |
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thread.start() |
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buffer = "" |
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for new_text in streamer: |
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buffer += new_text |
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yield buffer |
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chatbot = gr.Chatbot(height=500) |
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with gr.Blocks(css=CSS) as demo: |
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gr.HTML(TITLE) |
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gr.HTML(DESCRIPTION) |
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gr.ChatInterface( |
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fn=stream_chat, |
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chatbot=chatbot, |
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fill_height=True, |
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theme="soft", |
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retry_btn=None, |
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undo_btn="Delete Previous", |
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clear_btn="Clear", |
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additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), |
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additional_inputs=[ |
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gr.Slider( |
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minimum=0, |
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maximum=1, |
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step=0.1, |
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value=0.8, |
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label="Temperature", |
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render=False, |
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), |
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gr.Slider( |
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minimum=128, |
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maximum=4096, |
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step=1, |
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value=1024, |
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label="Max new tokens", |
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render=False, |
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), |
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gr.Slider( |
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minimum=0.0, |
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maximum=1.0, |
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step=0.1, |
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value=0.8, |
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label="top_p", |
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render=False, |
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), |
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gr.Slider( |
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minimum=1, |
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maximum=20, |
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step=1, |
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value=20, |
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label="top_k", |
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render=False, |
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), |
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gr.Slider( |
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minimum=0.0, |
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maximum=2.0, |
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step=0.1, |
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value=1.2, |
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label="Repetition penalty", |
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render=False, |
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), |
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], |
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examples=[ |
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["Explain Deep Learning as a pirate."], |
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["Give me five ideas for a child's summer science project."], |
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["Provide advice for writing a script for a puzzle game."], |
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["Create a tutorial for building a breakout game using markdown."], |
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["超能力を持つ主人公のSF物語のシナリオを考えてください。伏線の設定、テーマやログラインを理論的に使用してください"], |
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["子供の夏休みの自由研究のための、5つのアイデアと、その手法を簡潔に教えてください。"], |
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["パズルゲームのスクリプト作成のためにアドバイスお願いします"], |
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["マークダウン記法にて、ブロック崩しのゲーム作成の教科書作成してください"], |
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["お笑いのトンチ大会のお題を考えてください"], |
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["日本語の慣用句、ことわざについての試験問題を考えてください"], |
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], |
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cache_examples=False, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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