이 문서는 https://huggingface.co/google/gemma-1.1-2b-it 기반으로 제작되었습니다.

필요한 패키지 설치

아래 3개의 패키지를 설치합니다. 만약에 경고 메시지에 런타임 재시작해야한다는 메시지가 나온다면 [메뉴] > [런타임] > [세션 다시 시작]을 클릭하여 런타임을 재시작합니다.

!pip install torch          # PyTorch, 딥러닝 프레임워크를 설치합니다.
!pip install transformers   # Hugging Face의 Transformers 라이브러리를 설치합니다.
!pip install accelerate     # Hugging Face의 Accelerate 라이브러리를 설치합니다.

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허깅페이스 접근 토큰 발행

Hugging Face 인증을 위한 환경 변수를 설정합니다. ‘HF_TOKEN’은 Hugging Face 토큰을 저장하고 있는 것으로 가정한 사용자 특정 환경 변수입니다.

«이미지 삽입»

허깅페이스 접근 토큰 설정

허깅페이스 접근 토큰을 발행하였다면 ‘HF_TOKEN’이름으로 환경 변수로 등록합니다. 코랩에서 좀 더 쉽게 사용할 수 있도록 코랩의 “보안 비밀” 기능을 사용하여 키 값을 가지고 옵니다. 이렇게 하면 소스코드 공유 시에 키를 공유하지 않아도 되며, 다른 코랩 소스코드 사용할 때도 재사용이 용이합니다.

import os
from google.colab import userdata

os.environ["HF_TOKEN"] = userdata.get('HF_TOKEN')

모델 준비하기

“google/gemma-1.1-2b-it”에 대한 토크나이저와 모델을 준비합니다. 아래와 같이 필요한 파일을 다운로드 받습니다. 가장 큰 파일이 4.95G 정도 되네요.

«이미지 삽입»

# Hugging Face의 Transformers 라이브러리에서 토크나이저와 모델 클래스를 임포트합니다.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Hugging Face 모델 저장소에서 토크나이저와 모델을 로드합니다.
# 모델 식별자 'google/gemma-1.1-2b-it'는 로드할 모델을 지정합니다.
tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-2b-it")
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-1.1-2b-it",   # 모델 식별자
    device_map="auto",          # 모델을 사용 가능한 장치(GPU/CPU)에 자동으로 매핑합니다.
    torch_dtype=torch.float16,  # 메모리 사용을 줄이고 빠르게 계산하기 위해 데이터 타입을 float16으로 설정합니다.
    revision="float16",         # float16을 지원하는 특정 모델 리비전을 사용합니다.
)

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# 모델이 머신 러닝에 관한 시를 작성하도록 입력 텍스트를 정의합니다.
input_text = "Write me a poem about Machine Learning."

# 입력 텍스트를 토크나이즈하고 텐서로 준비합니다.
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") # PyTorch 텐서로 변환하고 CUDA(GPU)로 보냅니다.

# input_ids를 기반으로 모델에서 토큰 시퀀스를 생성합니다.
outputs = model.generate(**input_ids)

# 생성된 토큰을 문자열로 디코딩하고 출력합니다.
print(tokenizer.decode(outputs[0]))

/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1178: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
  warnings.warn(


<bos>Write me a poem about Machine Learning.

In circuits of logic, a mind unseen,

위 코드 실행 결과가 아래 처럼 출력되다가 중단되어 나옵니다.

<bos>Write me a poem about Machine Learning.

In circuits of logic, a mind unseen,

그 이유는 model.generate() 함수에서 max_length의 기본값이 20으로 설정되어 있기 때문입니다.

max_length란 (입력 텍스트의 길이 + 출력 텍스트의 길이)의 합의 최대 토큰 수를 정의합니다. 즉 입력과 출력을 모두 더해서 max_length을 넘기지 않도록 생성됩니다.

모델의 결과가 충분히 작성될 수 있도록 max_length를 512로 설정하여 다시 호출해봅니다.

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

# input_ids를 기반으로 모델에서 토큰 시퀀스를 생성합니다.
# 생성된 시퀀스는 입력 길이도 포함하여 최대 512 토큰 길이를 가집니다.
outputs = model.generate(**input_ids, max_length=512)
print(tokenizer.decode(outputs[0]))

<bos>Write me a poem about Machine Learning.

In circuits of logic, a mind unseen,
A tapestry of data, a world serene.
Algorithms dance, a symphony of code,
Learning from experience, a journey bold.

From images that speak, to numbers that bind,
Machines learn, their knowledge entwined.
Predictive models, with foresight so bright,
Insights hidden, in the digital night.

In self-driving cars, their sensors keen,
Predicting paths, a seamless scene.
Natural language processing, a bridge so wide,
Understanding words, their meaning inside.

But with power comes responsibility,
Bias lurking, in the algorithms' abyss.
Transparency elusive, a challenge to face,
As machines learn, their consequences embrace.

Yet, in this labyrinth of digital dreams,
A future emerges, both wondrous and streams.
A world where machines and humans unite,
Creating a harmony, a technological delight.<eos>

정상적으로 결과가 출력되는 것을 확인할 수 있습니다. <eos> 토큰이 마지막에 출력되는데요. 이 특수토큰에 대한 설명은 아래와 같습니다.

<bos>와 <eos>는 특수한 토큰(token)으로, 각각 “beginning of sequence”와 “end of sequence”를 의미합니다. 이 토큰들은 자연어 처리에서 문장이나 텍스트의 시작과 끝을 나타내는 데 사용됩니다. 모델이 텍스트를 생성하거나 해석할 때 이 토큰들을 기준으로 문장의 시작과 끝을 구분하게 됩니다.

<bos>: 문장이나 텍스트 입력의 시작 부분에 사용되며, 모델이 새로운 텍스트를 생성하기 시작할 때의 시작점으로 사용됩니다.
<eos>: 문장이나 텍스트 입력의 끝 부분에 사용되며, 모델이 텍스트 생성을 중단해야 할 시점을 나타냅니다.

이러한 토큰들은 모델이 주어진 입력에 대해 문맥을 더 잘 이해하고, 적절한 길이의 출력을 생성하는 데 도움을 줍니다. 예를 들어, 텍스트를 생성하는 언어 모델에서 <eos> 토큰이 등장하면, 모델은 그 시점에서 문장 생성을 멈추고 그것을 출력의 끝으로 간주합니다.

코드 작성 테스트

문자열에서 각 단어의 빈도수를 세는 파이썬 코드를 작성해달라고 요청해보겠습니다. 문자열은 앞서 생성했던 시를 예시로 들어보겠습니다.

text = """
In circuits of logic, a mind unseen,
A tapestry of data, a world serene.
Algorithms dance, a symphony of code,
Learning from experience, a journey bold.
"""

input_text = f"Write a Python code to count the frequency of each word in the {text} and print the words in descending order of frequency."

input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids, max_length=512)
print(tokenizer.decode(outputs[0]))

<bos>Write a Python code to count the frequency of each word in the 
In circuits of logic, a mind unseen,
A tapestry of data, a world serene.
Algorithms dance, a symphony of code,
Learning from experience, a journey bold.
 and print the words in descending order of frequency.

```python
from collections import Counter

text = """
In circuits of logic, a mind unseen,
A tapestry of data, a world serene.
Algorithms dance, a symphony of code,
Learning from experience, a journey bold.
"""

# Count the frequency of each word
word_counts = Counter(text.split())

# Sort the words in descending order of frequency
sorted_words = word_counts.most_common(10)

# Print the words
for word, count in sorted_words:
    print(f"{word}: {count}")
```

Output:

```
Algorithms: 3
code: 2
data: 2
journey: 2
mind: 2
process: 1
sequence: 1
tapestries: 1
"""
```<eos>

파이썬 코드블록을 사용하여 파이썬 코드가 생성되었습니다. 그럼 생성된 코드가 제대로 동작하는 지 테스트해보겠습니다.

from collections import Counter

text = """
In circuits of logic, a mind unseen,
A tapestry of data, a world serene.
Algorithms dance, a symphony of code,
Learning from experience, a journey bold.
"""

# Count the frequency of each word
word_counts = Counter(text.split())

# Sort the words in descending order of frequency
sorted_words = word_counts.most_common(10)

# Print the words
for word, count in sorted_words:
    print(f"{word}: {count}")

a: 4
of: 3
In: 1
circuits: 1
logic,: 1
mind: 1
unseen,: 1
A: 1
tapestry: 1
data,: 1

정상적으로 작동됩니다. 그럼 이제 전체 시를 입력하고 테스트 해보겠습니다.

from collections import Counter

text = """
In circuits of logic, a mind unseen,
A tapestry of data, a world serene.
Algorithms dance, a symphony of code,
Learning from experience, a journey bold.

From images that speak, to numbers that bind,
Machines learn, their knowledge entwined.
Predictive models, with foresight so bright,
Insights hidden, in the digital night.

In self-driving cars, their sensors keen,
Predicting paths, a seamless scene.
Natural language processing, a bridge so wide,
Understanding words, their meaning inside.

But with power comes responsibility,
Bias lurking, in the algorithms' abyss.
Transparency elusive, a challenge to face,
As machines learn, their consequences embrace.

Yet, in this labyrinth of digital dreams,
A future emerges, both wondrous and streams.
A world where machines and humans unite,
Creating a harmony, a technological delight.
"""

# Count the frequency of each word
word_counts = Counter(text.split())

# Sort the words in descending order of frequency
sorted_words = word_counts.most_common(10)

# Print the words
for word, count in sorted_words:
    print(f"{word}: {count}")

a: 9
of: 4
their: 4
A: 3
in: 3
In: 2
world: 2
that: 2
to: 2
learn,: 2

마무리

이번에 릴리즈된 gemma 1.1 2b it 모델을 테스트 해봤습니다. 온디바이스나 간단한 태스크 처리에 2B 모델의 활약이 기대되는 만큼 앞으로 여러가지 테스트를 해보면서 활용 사례를 늘려가보겠습니다.

RLHF 튜닝으로 향상된 Gemma 1.1 2B IT 공개

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