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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 spaces |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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DESCRIPTION = "# CALM2-7B-chat" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" |
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MAX_MAX_NEW_TOKENS = 2048 |
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DEFAULT_MAX_NEW_TOKENS = 1024 |
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "32768")) |
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if torch.cuda.is_available(): |
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model_id = "cyberagent/calm2-7b-chat" |
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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def apply_chat_template(conversation: list[dict[str, str]]) -> str: |
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prompt = "\n".join([f"{c['role']}: {c['content']}" for c in conversation]) |
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prompt = f"{prompt}\nASSISTANT: " |
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return prompt |
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@spaces.GPU |
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@torch.inference_mode() |
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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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max_new_tokens: int = 1024, |
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temperature: float = 0.7, |
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top_p: float = 0.95, |
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top_k: int = 50, |
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repetition_penalty: float = 1.0, |
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) -> Iterator[str]: |
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conversation = [] |
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for user, assistant in chat_history: |
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conversation.extend([{"role": "USER", "content": user}, {"role": "ASSISTANT", "content": assistant}]) |
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conversation.append({"role": "USER", "content": message}) |
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prompt = apply_chat_template(conversation) |
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input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") |
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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 conversation 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=10.0, 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=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=repetition_penalty, |
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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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demo = gr.ChatInterface( |
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fn=generate, |
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type="tuples", |
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additional_inputs_accordion=gr.Accordion(label="詳細設定", open=False), |
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additional_inputs=[ |
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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=4.0, |
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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=0.95, |
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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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gr.Slider( |
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label="Repetition penalty", |
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minimum=1.0, |
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maximum=2.0, |
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step=0.05, |
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value=1.0, |
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), |
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], |
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stop_btn=None, |
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examples=[ |
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["東京の観光名所を教えて。"], |
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["落武者って何？"], |
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["暴れん坊将軍って誰のこと？"], |
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["人がヘリを食べるのにかかる時間は？"], |
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], |
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description=DESCRIPTION, |
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css_paths="style.css", |
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fill_height=True, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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