test.py

tools = [
    {
        "name": "track",
        "description": "追踪指定股票的实时价格",
        "parameters": {
            "type": "object",
            "properties": {
                "symbol": {
                    "description": "需要追踪的股票代码"
                }
            },
            "required": ['symbol']
        }
    },
    {
        "name": "text-to-speech",
        "description": "将文本转换为语音",
        "parameters": {
            "type": "object",
            "properties": {
                "text": {
                    "description": "需要转换成语音的文本"
                },
                "voice": {
                    "description": "要使用的语音类型（男声、女声等）"
                },
                "speed": {
                    "description": "语音的速度（快、中等、慢等）"
                }
            },
            "required": ['text']
        }
    }
]
system_info = {"role": "system", "content": "Answer the following questions as best as you can. You have access to the following tools:", "tools": tools}

import os
import platform
from transformers import AutoTokenizer, AutoModel
import torch

MODEL_PATH = os.environ.get('MODEL_PATH', '../../chatglm3-6b')
TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
if 'cuda' in DEVICE: # AMD, NVIDIA GPU can use Half Precision
    model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True).to(DEVICE).eval()
else: # CPU, Intel GPU and other GPU can use Float16 Precision Only
    model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True).float().to(DEVICE).eval()

history = [system_info]
query = "帮我查询股票10111的价格"
response, history = model.chat(tokenizer, query, history=history)
print(response)