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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 |
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MAX_MAX_NEW_TOKENS = 4096 |
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DEFAULT_MAX_NEW_TOKENS = 2048 |
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) |
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DESCRIPTION = """\ |
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# hon9kon9ize/Cantonese-Llama-2-7B-preview20240903 |
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Please join our [Discord server](https://discord.gg/gG6GPp8XxQ) and give me your feedback |
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""" |
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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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if torch.cuda.is_available(): |
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model_id = "hon9kon9ize/Cantonese-Llama-2-7B-preview20240903" |
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") |
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model = torch.compile(model) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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tokenizer.use_default_system_prompt = False |
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@spaces.GPU(queue=False) |
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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 = 2048, |
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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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repetition_penalty: float = 1.2, |
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) -> str: |
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conversation = [] |
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conversation.append({"role": "system", "content": system_prompt if system_prompt else "你係由 hon9kon9ize 開發嘅 CantoneseLLM，你係一個好幫得手嘅助理" }) |
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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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print(chat_history) |
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input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True, 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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output_ids = model.generate( |
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input_ids, |
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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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repetition_penalty=repetition_penalty |
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) |
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response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) |
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return response |
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chat_interface = gr.ChatInterface( |
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fn=generate, |
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additional_inputs=[ |
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gr.Textbox(label="System prompt", lines=6), |
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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=0.6, |
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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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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=1.2, |
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), |
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], |
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stop_btn=None, |
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examples=[ |
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["Hello there! How are you doing?"], |
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["咩嘢係氣候變化?"], |
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["香港最高嘅山係？"], |
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["邊個係香港特首？"], |
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["香港行政长官是谁？"] |
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], |
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) |
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with gr.Blocks() as demo: |
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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() |
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