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| #!/usr/bin/env python | |
| import os | |
| from threading import Thread | |
| from typing import Iterator | |
| import gradio as gr | |
| import spaces | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
| DESCRIPTION = "# CALM2-7B-chat" | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
| MAX_MAX_NEW_TOKENS = 2048 | |
| DEFAULT_MAX_NEW_TOKENS = 1024 | |
| MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "32768")) | |
| if torch.cuda.is_available(): | |
| model_id = "cyberagent/calm2-7b-chat" | |
| model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| def apply_chat_template(conversation: list[dict[str, str]]) -> str: | |
| prompt = "\n".join([f"{c['role']}: {c['content']}" for c in conversation]) | |
| prompt = f"{prompt}\nASSISTANT: " | |
| return prompt | |
| def generate( | |
| message: str, | |
| chat_history: list[tuple[str, str]], | |
| max_new_tokens: int = 1024, | |
| temperature: float = 0.7, | |
| top_p: float = 0.95, | |
| top_k: int = 50, | |
| repetition_penalty: float = 1.0, | |
| ) -> Iterator[str]: | |
| conversation = [] | |
| for user, assistant in chat_history: | |
| conversation.extend([{"role": "USER", "content": user}, {"role": "ASSISTANT", "content": assistant}]) | |
| conversation.append({"role": "USER", "content": message}) | |
| prompt = apply_chat_template(conversation) | |
| input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") | |
| if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: | |
| input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] | |
| gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") | |
| input_ids = input_ids.to(model.device) | |
| streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) | |
| generate_kwargs = dict( | |
| {"input_ids": input_ids}, | |
| streamer=streamer, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=True, | |
| top_p=top_p, | |
| top_k=top_k, | |
| temperature=temperature, | |
| num_beams=1, | |
| repetition_penalty=repetition_penalty, | |
| ) | |
| t = Thread(target=model.generate, kwargs=generate_kwargs) | |
| t.start() | |
| outputs = [] | |
| for text in streamer: | |
| outputs.append(text) | |
| yield "".join(outputs) | |
| chat_interface = gr.ChatInterface( | |
| fn=generate, | |
| chatbot=gr.Chatbot(show_label=False, layout="panel", height=600), | |
| additional_inputs_accordion_name="詳細設定", | |
| additional_inputs=[ | |
| gr.Slider( | |
| label="Max new tokens", | |
| minimum=1, | |
| maximum=MAX_MAX_NEW_TOKENS, | |
| step=1, | |
| value=DEFAULT_MAX_NEW_TOKENS, | |
| ), | |
| gr.Slider( | |
| label="Temperature", | |
| minimum=0.1, | |
| maximum=4.0, | |
| step=0.1, | |
| value=0.7, | |
| ), | |
| gr.Slider( | |
| label="Top-p (nucleus sampling)", | |
| minimum=0.05, | |
| maximum=1.0, | |
| step=0.05, | |
| value=0.95, | |
| ), | |
| gr.Slider( | |
| label="Top-k", | |
| minimum=1, | |
| maximum=1000, | |
| step=1, | |
| value=50, | |
| ), | |
| gr.Slider( | |
| label="Repetition penalty", | |
| minimum=1.0, | |
| maximum=2.0, | |
| step=0.05, | |
| value=1.0, | |
| ), | |
| ], | |
| stop_btn=None, | |
| examples=[ | |
| ["東京の観光名所を教えて。"], | |
| ["落武者って何？"], | |
| ["暴れん坊将軍って誰のこと？"], | |
| ["人がヘリを食べるのにかかる時間は？"], | |
| ], | |
| ) | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| gr.DuplicateButton( | |
| value="Duplicate Space for private use", | |
| elem_id="duplicate-button", | |
| visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
| ) | |
| chat_interface.render() | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() | |