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)
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