当前位置：首页 > article >正文

llama3.1 8b instruct的function calling的template解析

article 2024/9/29 19:04:57

最近在研究function calling，但是没有llama3.1 8b instruction调用function calling的教程，于是我顺手写了一个调用的例子，仅供大家学习参考，安装依赖：

pip install transformers
pip install langchain

调用代码示例：

import json

import torch
from langchain_core.prompts import PromptTemplate
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "<your path to >/llama3.1-8b-instruct"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
sys_message="""Cutting Knowledge Date: December 2023
Today Date: 23 July 2024

When you receive a tool call response, use the output to format an answer to the orginal user question.

You are a helpful assistant with tool calling capabilities.
"""

user_message="""
Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.

Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.

{{functions}}

Question: {{query}}
"""
prompt_template= PromptTemplate(template=user_message, input_variables=["functions", "query"], template_format="jinja2")
query = "what is the weather like in San Fransisco?"
functions = [{
    "type": "function",
    "function": {
    "name": "get_current_conditions",
    "description": "Get the current weather conditions for a specific location",
    "parameters": {
        "type": "object",
        "properties": {
        "location": {
            "type": "string",
            "description": "The city and state, e.g., San Francisco, CA"
        },
        "unit": {
            "type": "string",
            "enum": ["Celsius", "Fahrenheit"],
            "description": "The temperature unit to use. Infer this from the user's location."
        }
        },
        "required": ["location", "unit"]
    }
    }
}]
messages = [
    {"role": "system", "content": sys_message},
    {"role":"user", "content": prompt_template.format(query=query, functions=functions)}]
# inputs = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer.apply_chat_template(messages, return_dict=True, return_tensors="pt")
print(inputs)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, device_map="auto")
inputs = {k: v.to(model.device) for k, v in inputs.items()}
out = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(out[0][len(inputs["input_ids"][0]):]))