toolformer pytorch 다운로드 - toolformer pytorch 소스 코드 다운로드

toolformer pytorch

AI 소스 코드

0.0.30

다운로드

Toolformer - Pytorch(wip)

도구를 사용할 수 있는 언어 모델인 Toolformer의 MetaAI 구현

감사

Stability.ai 작업 및 오픈소스 최첨단 인공지능 연구에 대한 아낌없는 후원
다양한 도구의 초기 커밋을 통해 공을 굴려준 Enrico!
API 호출에 대한 함수와 매개변수를 구문 분석하기 위해 이 저장소에서 모든 정규식을 수행한 ChatGPT에 감사드립니다. 저는 정규 표현식을 잘 못해서 AI의 도움이 컸습니다(장애 없이 완벽했습니다).

설치하다

$ pip install toolformer-pytorch

용법

현재 날짜와 시간에 대한 인식을 언어 모델에 제공하는 사용 예입니다.

 import torch
from toolformer_pytorch import Toolformer , PaLM

# simple calendar api call - function that returns a string

def Calendar ():
    import datetime
    from calendar import day_name , month_name
    now = datetime . datetime . now ()
    return f'Today is { day_name [ now . weekday ()] } , { month_name [ now . month ] } { now . day } , { now . year } .'

# prompt for teaching it to use the Calendar function from above

prompt = f"""
Your task is to add calls to a Calendar API to a piece of text.
The API calls should help you get information required to complete the text.
You can call the API by writing "[Calendar()]"
Here are some examples of API calls:
Input: Today is the first Friday of the year.
Output: Today is the first [Calendar()] Friday of the year.
Input: The president of the United States is Joe Biden.
Output: The president of the United States is [Calendar()] Joe Biden.
Input: [input]
Output: 
"""

data = [
    "The store is never open on the weekend, so today it is closed." ,
    "The number of days from now until Christmas is 30" ,
    "The current day of the week is Wednesday."
]

# model - here using PaLM, but any nn.Module that returns logits in the shape (batch, seq, num_tokens) is fine

model = PaLM (
    dim = 512 ,
    depth = 2 ,
    heads = 8 ,
    dim_head = 64
). cuda ()

# toolformer

toolformer = Toolformer (
    model = model ,
    model_seq_len = 256 ,
    teach_tool_prompt = prompt ,
    tool_id = 'Calendar' ,
    tool = Calendar ,
    finetune = True
)

# invoking this will
# (1) prompt the model with your inputs (data), inserted into [input] tag
# (2) with the sampled outputs, filter out the ones that made proper API calls
# (3) execute the API calls with the `tool` given
# (4) filter with the specialized filter function (which can be used independently as shown in the next section)
# (5) fine-tune on the filtered results

filtered_stats = toolformer ( data )

# then, once you see the 'finetune complete' message

response = toolformer . sample_model_with_api_calls ( "How many days until the next new years?" )

# hopefully you see it invoke the calendar and utilize the response of the api call...

이 논문의 주요 참신함은 API 호출을 삽입하도록 지시된 변환기의 출력에 대한 적합성 점수를 정의하는 것입니다. 점수는 변환기를 미세 조정하여 뒤따르는 텍스트의 복잡성을 줄이는 API 호출을 만들기 위해 샘플링된 출력을 필터링하는 데 사용됩니다.

 import torch

from toolformer_pytorch import (
    Toolformer ,
    PaLM ,
    filter_tokens_with_api_response
)

# model

palm = PaLM (
    dim = 512 ,
    num_tokens = 20000 ,
    depth = 2 ,
    heads = 8 ,
    dim_head = 64
). cuda ()

# mock some tokens

mock_start_pos = 512
mock_api_call_length = 10
mock_api_start_id = 19998
mock_api_stop_id = 19999

tokens = torch . randint ( 0 , 20000 , ( 10 , 1024 )). cuda ()
tokens_with_api_response = torch . randint ( 0 , 20000 , ( 10 , 1024 )). cuda ()
tokens_without_api_response = torch . randint ( 0 , 20000 , ( 10 , 1024 )). cuda ()

tokens_with_api_response [:, mock_start_pos ] = mock_api_start_id
tokens_with_api_response [:, mock_start_pos + mock_api_call_length ] = mock_api_stop_id

tokens_without_api_response [:, mock_start_pos ] = mock_api_start_id
tokens_without_api_response [:, mock_start_pos + mock_api_call_length ] = mock_api_stop_id

# filter

filtered_results = filter_tokens_with_api_response (
    model = palm ,
    tokens = tokens ,
    tokens_with_api_response = tokens_with_api_response ,
    tokens_without_api_response = tokens_without_api_response ,
    filter_threshold = 1. ,
    api_start_token_id = mock_api_start_id ,
    api_end_token_id = mock_api_stop_id
)

언어 모델에 의해 생성된 문자열에서 도구를 호출하려면 invoke_tools 사용하세요.

 from toolformer_pytorch import invoke_tools

def inc ( i ):
    return i + 1

def dec ( i ):
    return i - 1

function_registry = dict (
    inc = inc ,
    dec = dec
)

text = 'make the following api calls: [inc(1)] and [dec(2)] and [ignored(3)]'

invoke_tools ( function_registry , text )

# make the following api calls: [inc(1) → 2] and [dec(2) → 1] and [ignored(3)]

토도

인용

 @inproceedings { Schick2023ToolformerLM ,
    title   = { Toolformer: Language Models Can Teach Themselves to Use Tools } ,
    author  = { Timo Schick and Jane Dwivedi-Yu and Roberto Dessi and Roberta Raileanu and Maria Lomeli and Luke Zettlemoyer and Nicola Cancedda and Thomas Scialom } ,
    year    = { 2023 }
}

 @article { Gao2022PALPL ,
    title   = { PAL: Program-aided Language Models } ,
    author  = { Luyu Gao and Aman Madaan and Shuyan Zhou and Uri Alon and Pengfei Liu and Yiming Yang and Jamie Callan and Graham Neubig } ,
    journal = { ArXiv } ,
    year    = { 2022 } ,
    volume  = { abs/2211.10435 }
}