--- language: - en - ko license: llama3 library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: - meta-llama/Meta-Llama-3-70B - jeiku/Average_Test_v1 - Bllossom/llama-3-Korean-Bllossom-70B --- # Bllossom | [Demo]() | [Homepage](https://www.bllossom.ai/) | [Github](https://github.com/MLP-Lab/Bllossom) | [Colab-tutorial](https://colab.research.google.com/drive/1fBOzUVZ6NRKk_ugeoTbAOokWKqSN47IG?usp=sharing) | ```bash 저희 Bllossom팀 에서 한국어-영어 이중 언어모델인 Bllossom을 공개했습니다! 서울과기대 슈퍼컴퓨팅 센터의 지원으로 100GB가넘는 한국어로 모델전체를 풀튜닝한 한국어 강화 이중언어 모델입니다! 한국어 잘하는 모델 찾고 있지 않으셨나요? - 한국어 최초! 무려 3만개가 넘는 한국어 어휘확장 - Llama3대비 대략 25% 더 긴 길이의 한국어 Context 처리가능 - 한국어-영어 Pararell Corpus를 활용한 한국어-영어 지식연결 (사전학습) - 한국어 문화, 언어를 고려해 언어학자가 제작한 데이터를 활용한 미세조정 - 강화학습 이 모든게 한꺼번에 적용되고 상업적 이용이 가능한 Bllossom을 이용해 여러분 만의 모델을 만들어보세욥! 본 모델은 42GB 이상 GPU 혹은 42GB 이상의 메모리가 있는 CPU에서 구동 가능한 양자화 모델입니다! 1. Bllossom-8B는 서울과기대, 테디썸, 연세대 언어자원 연구실의 언어학자와 협업해 만든 실용주의기반 언어모델입니다! 앞으로 지속적인 업데이트를 통해 관리하겠습니다 많이 활용해주세요 🙂 2. 초 강력한 Advanced-Bllossom 8B, 70B모델, 시각-언어모델을 보유하고 있습니다! (궁금하신분은 개별 연락주세요!!) 3. Bllossom은 NAACL2024, LREC-COLING2024 (구두) 발표로 채택되었습니다. 4. 좋은 언어모델 계속 업데이트 하겠습니다!! 한국어 강화를위해 공동 연구하실분(특히논문) 언제든 환영합니다!! 특히 소량의 GPU라도 대여 가능한팀은 언제든 연락주세요! 만들고 싶은거 도와드려요. ``` The Bllossom language model is a Korean-English bilingual language model based on the open-source LLama3. It enhances the connection of knowledge between Korean and English. It has the following features: * **Knowledge Linking**: Linking Korean and English knowledge through additional training * **Vocabulary Expansion**: Expansion of Korean vocabulary to enhance Korean expressiveness. * **Instruction Tuning**: Tuning using custom-made instruction following data specialized for Korean language and Korean culture * **Human Feedback**: DPO has been applied * **Vision-Language Alignment**: Aligning the vision transformer with this language model **This model developed by [MLPLab at Seoultech](http://mlp.seoultech.ac.kr), [Teddysum](http://teddysum.ai/) and [Yonsei Univ](https://sites.google.com/view/hansaemkim/hansaem-kim).** **This model was converted to GGUF format from [`Bllossom/llama-3-Korean-Bllossom-70B`](https://huggingface.co/Bllossom/llama-3-Korean-Bllossom-70B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Bllossom/llama-3-Korean-Bllossom-70B) for more details on the model.** ## Demo Video

Bllossom-V Demo

Bllossom Demo(Kakao)ㅤㅤㅤㅤㅤㅤㅤㅤ

## NEWS * [2024.05.08] Vocab Expansion Model Update * [2024.04.25] We released Bllossom v2.0, based on llama-3 * [2023/12] We released Bllossom-Vision v1.0, based on Bllossom * [2023/08] We released Bllossom v1.0, based on llama-2. * [2023/07] We released Bllossom v0.7, based on polyglot-ko. ## Example code ```python !CMAKE_ARGS="-DLLAMA_CUDA=on" pip install llama-cpp-python !huggingface-cli download Bllossom/llama-3-Korean-Bllossom-70B-gguf-Q4_K_M --local-dir='YOUR-LOCAL-FOLDER-PATH' from llama_cpp import Llama from transformers import AutoTokenizer model_id = 'Bllossom/llama-3-Korean-Bllossom-70B-gguf-Q4_K_M' tokenizer = AutoTokenizer.from_pretrained(model_id) model = Llama( model_path='YOUR-LOCAL-FOLDER-PATH/llama-3-Korean-Bllossom-70B-RAG-gguf-Q4_K_M.gguf', n_ctx=512, n_gpu_layers=-1 # Number of model layers to offload to GPU ) PROMPT = \ '''당신은 유용한 AI 어시스턴트입니다. 사용자의 질의에 대해 친절하고 정확하게 답변해야 합니다. You are a helpful AI assistant, you'll need to answer users' queries in a friendly and accurate manner.''' instruction = 'Your Instruction' messages = [ {"role": "system", "content": f"{PROMPT}"}, {"role": "user", "content": f"{instruction}"} ] prompt = tokenizer.apply_chat_template( messages, tokenize = False, add_generation_prompt=True ) generation_kwargs = { "max_tokens":512, "stop":["<|eot_id|>"], "echo":True, # Echo the prompt in the output "top_p":0.9, "temperature":0.6, } resonse_msg = model(prompt, **generation_kwargs) print(resonse_msg['choices'][0]['text'][len(prompt):]) ``` ## Citation **Language Model** ```text @misc{bllossom, author = {ChangSu Choi, Yongbin Jeong, Seoyoon Park, InHo Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim}, title = {Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean}, year = {2024}, journal = {LREC-COLING 2024}, paperLink = {\url{https://arxiv.org/pdf/2403.10882}}, }, } ``` **Vision-Language Model** ```text @misc{bllossom-V, author = {Dongjae Shin, Hyunseok Lim, Inho Won, Changsu Choi, Minjun Kim, Seungwoo Song, Hangyeol Yoo, Sangmin Kim, Kyungtae Lim}, title = {X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment}, year = {2024}, publisher = {GitHub}, journal = {NAACL 2024 findings}, paperLink = {\url{https://arxiv.org/pdf/2403.11399}}, }, } ``` ## Contact - 임경태(KyungTae Lim), Professor at Seoultech. `ktlim@seoultech.ac.kr` - 함영균(Younggyun Hahm), CEO of Teddysum. `hahmyg@teddysum.ai` - 김한샘(Hansaem Kim), Professor at Yonsei. `khss@yonsei.ac.kr` ## Contributor - 최창수(Chansu Choi), choics2623@seoultech.ac.kr - 김상민(Sangmin Kim), sangmin9708@naver.com - 원인호(Inho Won), wih1226@seoultech.ac.kr - 김민준(Minjun Kim), mjkmain@seoultech.ac.kr - 송승우(Seungwoo Song), sswoo@seoultech.ac.kr - 신동재(Dongjae Shin), dylan1998@seoultech.ac.kr - 임현석(Hyeonseok Lim), gustjrantk@seoultech.ac.kr - 육정훈(Jeonghun Yuk), usually670@gmail.com - 유한결(Hangyeol Yoo), 21102372@seoultech.ac.kr - 송서현(Seohyun Song), alexalex225225@gmail.com