62 lines
3.3 KiB
Markdown
62 lines
3.3 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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base_model: mistralai/Mistral-Nemo-Instruct-2407
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datasets:
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- Saxo/ko_cn_translation_tech_social_science_linkbricks_single_dataset
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- Saxo/ko_jp_translation_tech_social_science_linkbricks_single_dataset
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- Saxo/en_ko_translation_tech_science_linkbricks_single_dataset_with_prompt_text_huggingface
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- Saxo/en_ko_translation_social_science_linkbricks_single_dataset_with_prompt_text_huggingface
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- Saxo/ko_aspect_sentiment_sns_mall_sentiment_linkbricks_single_dataset_with_prompt_text_huggingface
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- Saxo/ko_summarization_linkbricks_single_dataset_with_prompt_text_huggingface
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- Saxo/OpenOrca_cleaned_kor_linkbricks_single_dataset_with_prompt_text_huggingface
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- Saxo/ko_government_qa_total_linkbricks_single_dataset_with_prompt_text_huggingface_sampled
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- Saxo/ko-news-corpus-1
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- Saxo/ko-news-corpus-2
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- Saxo/ko-news-corpus-3
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- Saxo/ko-news-corpus-4
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- Saxo/ko-news-corpus-5
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- Saxo/ko-news-corpus-6
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- Saxo/ko-news-corpus-7
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- Saxo/ko-news-corpus-8
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- Saxo/ko-news-corpus-9
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- maywell/ko_Ultrafeedback_binarized
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- youjunhyeok/ko-orca-pair-and-ultrafeedback-dpo
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- lilacai/glaive-function-calling-v2-sharegpt
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- kuotient/gsm8k-ko
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language:
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- ko
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- en
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- jp
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- cn
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pipeline_tag: text-generation
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---
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# Model Card for Model ID
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<div align="center">
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<img src="http://www.linkbricks.com/wp-content/uploads/2024/11/fulllogo.png" />
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</div>
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AI 전문 기업인 Linkbricks Horizon-AI 의 데이터사이언티스트인 지윤성(Saxo) 대표가 <br>
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Mistral-Nemo-Instruct-2407 베이스모델을 사용해서 H100-80G 8개를 통해 약 20%정도의 파라미터를 한국어 CPT(Continued-Pretraining)->SFT->DPO 한 한글 언어 모델<br>
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천만건의 한글 뉴스 코퍼스를 기준으로 다양한 테스크별 한국어-중국어-영어-일본어 교차 학습 데이터와 수학 및 논리판단 데이터를 통하여 한중일영 언어 교차 증강 처리와 복잡한 논리 문제 역시 대응 가능하도록 훈련한 모델이다.<br>
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-토크나이저는 단어 확장 없이 베이스 모델 그대로 사용<br>
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-고객 리뷰나 소셜 포스팅 고차원 분석 및 코딩과 작문, 수학, 논리판단 등이 강화된 모델<br>
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-128k-Context Window<br>
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-한글 Function Call 및 Tool Calling 지원 <br>
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-Deepspeed Stage=3, rslora 및 BAdam Layer Mode 사용 <br><br><br>
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Finetuned by CEO Yunsung Ji (Saxo), a data scientist at Linkbricks Horizon-AI, a company specializing in AI and big data analytics <br>
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about 20% of total parameters Korean CPT(Continued-Pretraining)->SFT->DPO training model based on Mistral-Nemo-Instruct-2407 through 8 H100-80Gs as a Korean language model <br>
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It is a model that has been trained to handle Korean-Chinese-English-Japanese cross-training data and 10M korean news corpus and logic judgment data for various tasks to enable cross-fertilization processing and complex Korean logic & math problems. <br>
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-Tokenizer uses the base model without word expansion<br>
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-Models enhanced with high-dimensional analysis of customer reviews and social posts, as well as coding, writing, math and decision making<br>
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-128k-Context Window<br>
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-Support for Korean Functioncall and Tool Calling<br>
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-Deepspeed Stage=3, use rslora and BAdam Layer Mode<br>
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<br><br>
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<a href="www.horizonai.ai">www.horizonai.ai</a>, <a href="www.linkbricks.com">www.linkbricks.com</a>, <a href="www.linkbricks.vc">www.linkbricks.vc</a>
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