Tools should be a list of functions stored in json format

Pemanggilan fungsi di Phi-4-mini

Pemanggilan fungsi pertama kali muncul di keluarga Phi Family, dan sekarang Anda bisa menggunakannya melalui Phi-4-mini.

Contoh ini menunjukkan simulasi hasil Premier League. Tujuannya adalah agar Phi-4-mini dapat memberikan informasi pertandingan secara real-time. Berikut adalah contoh kodenya:

import torch
import json
import random
import string
import re
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig,pipeline,AutoTokenizer

model_path = "Your Phi-4-mini location"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="cuda",
    attn_implementation="flash_attention_2",
    torch_dtype="auto",
    trust_remote_code=True)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

# Tools should be a list of functions stored in json format
tools = [
    {
        "name": "get_match_result",
        "description": "get match result",
        "parameters": {
            "match": {
                "description": "The name of the match",
                "type": "str",
                "default": "Arsenal vs ManCity"
            }
        }
    },
]

# Function implementations

def get_match_result(match: str) -> str:
    # This would be replaced by a weather API
    match_data = {
        "Arsenal vs ManCity": "1:1",
        "Chelsea vs ManUnited": "0:2"
    }
    return match_data.get(match, "I don't know")


messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant",
        "tools": json.dumps(tools), # pass the tools into system message using tools argument
    },
    {
        "role": "user",
        "content": "What is the result of Arsenal vs ManCity today?"
    }
]

inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_dict=True, return_tensors="pt")

inputs = {k: v.to(model.device) for k, v in inputs.items()}
output = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(output[0][len(inputs["input_ids"][0]):]))

tokenizer.batch_decode(output)

response = tokenizer.decode(output[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)

tool_call_id = ''.join(random.choices(string.ascii_letters + string.digits, k=9))

messages.append({"role": "assistant", "tool_calls": [{"type": "function", "id": tool_call_id, "function": response}]})

try :
    tool_call = json.loads(response)[0]

except :
    json_part = re.search(r'\[.*\]', response, re.DOTALL).group(0)

    tool_call = json.loads(json_part)[0]


function_name = tool_call["name"]   

arguments = tool_call["arguments"]

result = get_match_result(**arguments) 

messages.append({"role": "tool", "tool_call_id": tool_call_id, "name": "get_match_result", "content": str(result)})

print(messages)

Penafian:
Dokumen ini telah diterjemahkan menggunakan layanan terjemahan AI Co-op Translator. Meskipun kami berupaya untuk mencapai akurasi, harap diperhatikan bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang sahih. Untuk informasi penting, disarankan menggunakan terjemahan profesional oleh manusia. Kami tidak bertanggung jawab atas kesalahpahaman atau penafsiran yang keliru yang timbul dari penggunaan terjemahan ini.