SeanJIE250/llama2_chatbot_law

Contact Information

Email:zhengjie1sun@gmail.com

English INTRO

Give me some red stars ♥️ if u like this model! It's the model focused on Law field, honestly,doing bad as a daily chatbot however,start to know Mandarin and can handle the case study in details.

Mandarin INTRO

老玩家点点红星♥️！中文法律对话机器人，具体案件审理较为不错。

Usage

First at first , implementing this command needs transformer library ,you can do the download directly.Hope u well!

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "SeanJIE250/chatbot_LAW"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "杀了人在中国判多少年？"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
outputs = model.generate(input_ids.to('cuda'),max_new_tokens=200)//you can adjust the max_new_tokens as you want.
response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=False)

print(response)

messages = [
    {"role": "user", "content": "How to split the property if I divorced with my handsband?"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
outputs = model.generate(input_ids.to('cuda'),max_new_tokens=200)//you can adjust the max_new_tokens as you want.
response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=False)

print(response)