BAAR1-150M
is a lightweight multilingual language model designed for fast, resource-efficient inference across 11 languages. Optimized for low latency and minimal compute environments, it enables responsive real-time generation on edge devices and standard cloud GPUs.
BAAR1-150M
은 11개 언어를 지원하는 초경량 다국어 언어 모델입니다. 저사양 하드웨어 및 리소스 제약 환경에서도 빠르고 안정적인 동작이 가능하도록 경량화되어, 실시간 텍스트 생성 환경에 최적화되어 있습니다.
⚠️
Experimental Test Release / 실험적 테스트 모델
This checkpoint is an
experimental test build (Proof-of-Concept)
released strictly for latency evaluation, research, and multilingual inference testing. Outputs may exhibit incomplete phrasing or factual errors and are not intended for production deployments.
본 가중치는 다국어 추론 지연 시간 검증 및 기술 실증을 위한 실험용 테스트 체크포인트(PoC)입니다. 프로토타입 단계이므로 사실 관계 왜곡이나 문장 미완성이 발생할 수 있으며, 상용 배포용이 아닌 연구 및 성능 검증 목적으로 공개되었습니다.
📊 Live Sample Inference (11개국 실시간 추론 테스트 결과)
Hardware: Single NVIDIA Tesla T4 | FP16 Inference
Language
Prompt
Generated Output
Latency
KO
(Korean)
넌 누구니?
AI로서 나는 물리적인 존재이며, 제가 할 수 있는 일은
~614.5ms
EN
(English)
What is artificial intelligence?
Artificial intelligence, often abbreviated as AI
~687.4ms
JA
(Japanese)
日本の首都はどこですか?
日本の首見ることは東京ですが約 20 , 587 km
~702.8ms
ZH
(Chinese)
人工智能的主要特点是什么?
人人的主要思想是理解和解释人类语言和行为的能力
~782.3ms
ES
(Spanish)
¿Cuál es la capital de España?
La capital del España, también conocida como la capital de
~511.2ms
PT
(Portuguese)
Qual é a capital do Brasil?
A capital do Brasil, também conocida como Canciller, é
~513.1ms
DE
(German)
Was ist künstliche Intelligenz?
Künstliche Intelligenz, auch bekannt
~521.5ms
RU
(Russian)
Какая столица России?
Столица России, штат Калифорния, является
~522.0ms
HI
(Hindi)
भारत की राजधानी क्या है?
भारत, जनमंद और बढसे संबंधित है, एक देश जो अपने इतिहास
~518.2ms
VI
(Vietnamese)
Thủ đô của Việt Nam là gì?
Việt Nam, còn được gọi là Chiến tranh Tây
~536.6ms
TH
(Thai)
เมืองหลวงของประเทศไทยคืออะไร?
เมืองที่มีชีวิตชลีของประเทศคือโตเกียว
~343.6ms
⚠️ Notes (안내 사항)
Pre-trained Model
: This model is a base pre-trained checkpoint. For specific tasks, factual alignment, or structured dialogue, task-specific fine-tuning (SFT) or Retrieval-Augmented Generation (RAG) is recommended.
기초 사전학습 모델
: 본 가중치는 사전학습 단계의 베이스 모델입니다. 특정 도메인 작업, 사실 관계 검증 및 정교한 대화 생성이 필요한 경우 파인튜닝(SFT) 또는 검색 증강 생성(RAG) 파이프라인과의 연계를 권장합니다.
💼 Opportunities & Contact (채용 제안 및 투자 문의)
This project demonstrates practical competency in custom model architecture design, end-to-end distributed training optimization, and efficient multi-language serving under compute constraints.
I am actively seeking
AI Engineering / Research opportunities, team recruitment offers, and project investment/partnerships
.
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