Pixtral 12B 모델 소개
미스트랄(Mistral)의 사용자 커뮤니티에서 텍스트와 이미지를 동시에 처리할 수 있는 첫 번째 멀티모달 모델, Pixtral 12B를 출시했습니다. 이 모델은 12B 규모의 크기로, 약 24GB의 용량입니다. 모델의 공개 및 배포는 mistral-community Hugging Face 및 magnet 주소(torrent)를 통해 진행하고 있습니다.
Pixtral 12B는 Mistral의 기존 텍스트 모델인 Nemo 12B를 기반으로 개발되었습니다. 이 모델은 이미지를 입력받아 이미지에 대한 질문에 답할 수 있습니다. Pixtral 12B는 이미지 캡셔닝 및 사진 속 객체 수 계산 등 다양한 작업을 수행할 수 있을 것으로 기대됩니다. 이미지 제공 시에는 다른 모델이나 OpenAI 등과 같이, 이미지의 URL이나 Pillow 라이브러리를 사용한 이미지 객체, 또는 Base64 인코딩된 이미지 문자열을 제공할 수 있습니다.
Pixtral 12B 모델은 텍스트와 이미지를 함께 입력받아, 입력된 이미지를 분석하거나 설명할 수 있습니다. 예를 들어, 모델에 특정 동물의 이미지를 주고 해당 동물이 특정 장소에 살 수 있는지 묻는 질문을 할 수 있습니다.
다만, 미스트랄 사용자 커뮤니티에서는 Pixtral 12B가 학습에 사용한 이미지 데이터를 공개되지는 않았습니다. 대부분의 생성 AI 모델들은 웹 사이트를 비롯한 공공 데이터를 사용해 학습되지만, 이 데이터가 저작권이 있을 수 있다는 문제로 논란이 되고 있습니다. 일부 AI 모델 제공업체들은 “공정 사용” 권리를 주장하며 공공 데이터를 수집하지만, 저작권자들은 이에 반발하며 법적 조치를 취하고 있습니다.
주의사항
이 모델은 MistralAI가 공식적으로 공개 및 배포한 모델이 아닙니다.
모델 사용 방법
Pixtral 12B 모델을 사용하기 위해서는 Hugging Face Hub에서 모델 체크포인트를 다운로드하고, 필요한 패키지를 설치하여 사용할 수 있습니다.
사용 방법:
-
pip install --upgrade mistral_common명령어를 통해 필수 패키지를 설치합니다. -
모델 체크포인트를 다운로드하려면
huggingface_hub패키지를 사용하여 다운로드 가능합니다.# 아래 코드를 실행하기 전, `pip install huggingface-hub`로 huggingface-hub를 미리 설치해야 합니다. from huggingface_hub import snapshot_download snapshot_download(repo_id="mistral-community/pixtral-12b-240910", local_dir="(모델 저장 위치)") -
토큰화 및 이미지와 텍스트 입력을 처리할 수 있는 코드 예시가 제공됩니다.
또는, 다음 magnet 주소를 사용하여 모델을 받을 수도 있습니다:
Pixtral 12B 모델에 이미지를 제공하는 예시 코드
PIL.Image 객체 사용 예시
from mistral_common.protocol.instruct.messages import (
UserMessage,
TextChunk,
ImageURLChunk,
ImageChunk,
)
from PIL import Image
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
tokenizer = MistralTokenizer.from_model("pixtral")
image = Image.new('RGB', (64, 64))
# tokenize images and text
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text="Describe this image"),
ImageChunk(image=image),
]
)
],
model="pixtral",
)
)
tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images
# Count the number of tokens
print("# tokens", len(tokens))
print("# images", len(images))
Image URL 사용 예시
url_dog = "https://picsum.photos/id/237/200/300"
url_mountain = "https://picsum.photos/seed/picsum/200/300"
# tokenize image urls and text
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text="Can this animal"),
ImageURLChunk(image_url=url_dog),
TextChunk(text="live here?"),
ImageURLChunk(image_url=url_mountain),
]
)
],
model="pixtral",
)
)
tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images
# Count the number of tokens
print("# tokens", len(tokens))
print("# images", len(images))
Base64 인코딩된 이미지 사용 예시
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text="What is this?"),
ImageURLChunk(image_url="data:image/jpeg;base64,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"),
]
)
],
model="pixtral",
)
)
tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images
# Count the number of tokens
print("# tokens", len(tokens))
print("# images", len(images))
Pixtral 12B 모델 저장소
MistralAI의 Pixtral 12B magnet 주소 공지
https://x.com/MistralAI/status/1833758285167722836
이 글은 GPT 모델로 정리한 글을 바탕으로 한 것으로, 원문의 내용 또는 의도와 다르게 정리된 내용이 있을 수 있습니다. 관심있는 내용이시라면 원문도 함께 참고해주세요! 읽으시면서 어색하거나 잘못된 내용을 발견하시면 덧글로 알려주시기를 부탁드립니다.
파이토치 한국 사용자 모임
이 정리한 이 글이 유용하셨나요? 회원으로 가입하시면 주요 글들을 이메일
로 보내드립니다! (기본은 Weekly지만 Daily로 변경도 가능합니다.)
아래
쪽에 좋아요
를 눌러주시면 새로운 소식들을 정리하고 공유하는데 힘이 됩니다~ 