Saxo_-_Linkbricks-Horizon-AI-Korean-Pro-27B-gguf

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Linkbricks-Horizon-AI-Korean-Pro-27B - GGUF

• Model creator: https://huggingface.co/Saxo/
• Original model: https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-Pro-27B/

Original model description:

library_name: transformers license: apache-2.0 base_model: google/gemma-2-27b-it datasets:

• Saxo/ko_cn_translation_tech_social_science_linkbricks_single_dataset
• Saxo/ko_jp_translation_tech_social_science_linkbricks_single_dataset
• Saxo/en_ko_translation_tech_science_linkbricks_single_dataset_with_prompt_text_huggingface
• Saxo/en_ko_translation_social_science_linkbricks_single_dataset_with_prompt_text_huggingface
• Saxo/ko_aspect_sentiment_sns_mall_sentiment_linkbricks_single_dataset_with_prompt_text_huggingface
• Saxo/ko_summarization_linkbricks_single_dataset_with_prompt_text_huggingface
• Saxo/OpenOrca_cleaned_kor_linkbricks_single_dataset_with_prompt_text_huggingface
• Saxo/ko_government_qa_total_linkbricks_single_dataset_with_prompt_text_huggingface_sampled
• Saxo/ko-news-corpus-1
• Saxo/ko-news-corpus-2
• Saxo/ko-news-corpus-3
• Saxo/ko-news-corpus-4
• Saxo/ko-news-corpus-5
• Saxo/ko-news-corpus-6
• Saxo/ko-news-corpus-7
• Saxo/ko-news-corpus-8
• Saxo/ko-news-corpus-9
• maywell/ko_Ultrafeedback_binarized
• youjunhyeok/ko-orca-pair-and-ultrafeedback-dpo
• lilacai/glaive-function-calling-v2-sharegpt
• kuotient/gsm8k-ko language:
• ko
• en
• jp
• cn pipeline_tag: text-generation

---

Model Card for Model ID

<div align="center"> <img src="https://www.linkbricks.com/wp-content/uploads/2022/03/%E1%84%85%E1%85%B5%E1%86%BC%E1%84%8F%E1%85%B3%E1%84%87%E1%85%B3%E1%84%85%E1%85%B5%E1%86%A8%E1%84%89%E1%85%B3%E1%84%85%E1%85%A9%E1%84%80%E1%85%A9-2-1024x804.png" /> </div>

AI 와 빅데이터 분석 전문 기업인 Linkbricks의 데이터사이언티스트인 지윤성(Saxo) 박사가 <br> gemma-2-27b-it 베이스모델을 사용해서 H100-80G 8개를 통해 약 50%정도의 파라미터를 한국어 CPT(Continued-Pretraining)->SFT->DPO 한 한글 언어 모델<br> 9천만건의 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> -토크나이저는 단어 확장 없이 베이스 모델 그대로 사용<br> -고객 리뷰나 소셜 포스팅 고차원 분석 및 코딩과 작문, 수학, 논리판단 등이 강화된 모델<br> -128k-Context Window<br> -Deepspeed Stage=3, rslora 및 BAdam Layer Mode 사용 <br> -ollama run benedict/linkbricks-gemma2-27b-korean-advanced-q4 <br> -ollama run benedict/linkbricks-gemma2-27b-korean-advanced-q8 <br><br>

Finetuned by Mr. Yunsung Ji (Saxo), a data scientist at Linkbricks, a company specializing in AI and big data analytics <br> about 50% of total parameters Korean CPT(Continued-Pretraining)->SFT->DPO training model based on gemma-2-27b-it through 8 H100-80Gs as a Korean language model <br> It is a model that has been trained to handle Korean-Chinese-English-Japanese cross-training data and 90M korean news corpus and logic judgment data for various tasks to enable cross-fertilization processing and complex Korean logic & math problems. <br> -Tokenizer uses the base model without word expansion<br> -Models enhanced with high-dimensional analysis of customer reviews and social posts, as well as coding, writing, math and decision making<br> -128k-Context Window<br> -Deepspeed Stage=3, use rslora and BAdam Layer Mode<br> <br><br>

<a href="www.linkbricks.com">www.linkbricks.com</a>, <a href="www.linkbricks.vc">www.linkbricks.vc</a>