LLMMINE/TIPA-7B-TranditionalTask

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base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft

It should be noted that the TIPA, and training data for this model are all from Chinese, and support for other languages may not be sufficient. If you need to train a model specific to a particular language or for a general purpose, please refer to our paper and GitHub

This model is trained on the TIPA dataset, and its function is to directly output all corrected new text, in the same way as the original downstream task. If you want to view the MTIPA (Model of Output Position), please refer to the following website: MTIPA

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

model = PeftModel.from_pretrained(base_model, "LLMMINE/TIPA-7B-TranditionalTask")
def chat(text):
    system = (
        "纠正输入这段话中的错别字，直接给出纠正后的文本，无需任何解释\n"
    )
    messages = [
        {"role": "system", "content": system},
        {"role": "user", "content": text}
    ]
    text_input = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

    # print("Input to model:")
    # print(text_input)
    model_inputs = tokenizer([text_input], return_tensors="pt").to(model.device)

    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=512,
        temperature=0.01,
    )
    generated_ids = [
        output_ids[len(input_ids):]
        for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]

    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    # print("Model response:")
    # print(response)
    return response

def main():
    print("命令行聊天程序已启动。输入您的文本，或输入 'exit' 退出。")
    while True:
        user_input = input("您: ")
        if user_input.lower() in ['exit', 'quit']:
            print("程序已退出。")
            break
        if not user_input.strip():
            print("请输入文本。")
            continue
        response = chat(user_input)
        print("回复:", response)

if __name__ == '__main__':
    main()

Input:

花雨在镇上落了一整夜，这静寂的风暴覆盖了屋顶，堵住了房门，令露宿的动物窒息而死。如此多的花朵自天而降，天亮时大界小巷都覆上了一层绵密的花毯，人们得用铲子耙子清理出通道才能出殡。

Taken from One Hundred Years of Solitude (Cien Años de Soledad) And let 街 -> 界

Output:

花雨在镇上落了一整夜，这静寂的风暴覆盖了屋顶，堵住了房门，令露宿的动物窒息而死。如此多的花朵自天而降，天亮时大街小巷都覆上了一层绵密的花毯，人们得用铲子耙子清理出通道才能出殡。

Github | Paper