bllossom/llama-3-korean-bllossom-70b-gguf-q4_k_m overview

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      "en",
      "ko"
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    "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"
    ],
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      "language": [
        "en",
        "ko"
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      "license": "llama3",
      "library_name": "transformers",
      "tags": [
        "llama-cpp",
        "gguf-my-repo"
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      "base_model": [
        "meta-llama/Meta-Llama-3-70B",
        "jeiku/Average_Test_v1",
        "Bllossom/llama-3-Korean-Bllossom-70B"
      ]
    },
    "hero_image_url": "https://github.com/teddysum/bllossom/blob/main//bllossom_icon.png?raw=true",
    "summary": "``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? 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, Teddysum and Yonsei Univ.** **This model was converted to GGUF format from Bllossom/llama-3-Korean-Bllossom-70B` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.**",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nlanguage:\n- en\n- ko\nlicense: llama3\nlibrary_name: transformers\ntags:\n- llama-cpp\n- gguf-my-repo\nbase_model:\n- meta-llama/Meta-Llama-3-70B\n- jeiku/Average_Test_v1\n- Bllossom/llama-3-Korean-Bllossom-70B\n---\n\n\n<a href=\"https://github.com/MLP-Lab/Bllossom\">\n  <img src=\"https://github.com/teddysum/bllossom/blob/main//bllossom_icon.png?raw=true\" width=\"40%\" height=\"50%\">\n</a>\n\n# 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) |\n\n\n```bash\n저희 Bllossom팀 에서 한국어-영어 이중 언어모델인 Bllossom을 공개했습니다!\n서울과기대 슈퍼컴퓨팅 센터의 지원으로 100GB가넘는 한국어로 모델전체를 풀튜닝한 한국어 강화 이중언어 모델입니다!\n한국어 잘하는 모델 찾고 있지 않으셨나요?\n - 한국어 최초! 무려 3만개가 넘는 한국어 어휘확장\n - Llama3대비 대략 25% 더 긴 길이의 한국어 Context 처리가능\n - 한국어-영어 Pararell Corpus를 활용한 한국어-영어 지식연결 (사전학습)\n - 한국어 문화, 언어를 고려해 언어학자가 제작한 데이터를 활용한 미세조정\n - 강화학습\n이 모든게 한꺼번에 적용되고 상업적 이용이 가능한 Bllossom을 이용해 여러분 만의 모델을 만들어보세욥!\n본 모델은 42GB 이상 GPU 혹은 42GB 이상의 메모리가 있는 CPU에서 구동 가능한 양자화 모델입니다!\n\n1. Bllossom-8B는 서울과기대, 테디썸, 연세대 언어자원 연구실의 언어학자와 협업해 만든 실용주의기반 언어모델입니다! 앞으로 지속적인 업데이트를 통해 관리하겠습니다 많이 활용해주세요 🙂\n2. 초 강력한 Advanced-Bllossom 8B, 70B모델, 시각-언어모델을 보유하고 있습니다! (궁금하신분은 개별 연락주세요!!)\n3. Bllossom은 NAACL2024, LREC-COLING2024 (구두) 발표로 채택되었습니다.\n4. 좋은 언어모델 계속 업데이트 하겠습니다!! 한국어 강화를위해 공동 연구하실분(특히논문) 언제든 환영합니다!! \n   특히 소량의 GPU라도 대여 가능한팀은 언제든 연락주세요! 만들고 싶은거 도와드려요.\n```\n\nThe 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:\n\n* **Knowledge Linking**: Linking Korean and English knowledge through additional training\n* **Vocabulary Expansion**: Expansion of Korean vocabulary to enhance Korean expressiveness.\n* **Instruction Tuning**: Tuning using custom-made instruction following data specialized for Korean language and Korean culture\n* **Human Feedback**: DPO has been applied\n* **Vision-Language Alignment**: Aligning the vision transformer with this language model \n\n**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).** \n**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.\nRefer to the [original model card](https://huggingface.co/Bllossom/llama-3-Korean-Bllossom-70B) for more details on the model.**\n\n\n## Demo Video\n\n<div style=\"display: flex; justify-content: space-between;\">\n  <!-- 첫 번째 컬럼 -->\n  <div style=\"width: 49%;\">\n    <a>\n      <img src=\"https://github.com/lhsstn/lhsstn/blob/main/x-llava_dem.gif?raw=true\" style=\"width: 100%; height: auto;\">\n    </a>\n    <p style=\"text-align: center;\">Bllossom-V Demo</p>\n  </div>\n\n  <!-- 두 번째 컬럼 (필요하다면) -->\n  <div style=\"width: 49%;\">\n    <a>\n      <img src=\"https://github.com/lhsstn/lhsstn/blob/main/bllossom_demo_kakao.gif?raw=true\" style=\"width: 70%; height: auto;\">\n    </a>\n    <p style=\"text-align: center;\">Bllossom Demo(Kakao)ㅤㅤㅤㅤㅤㅤㅤㅤ</p>\n  </div>\n</div>\n\n\n\n## NEWS\n* [2024.05.08] Vocab Expansion Model Update\n* [2024.04.25] We released Bllossom v2.0, based on llama-3\n* [2023/12] We released Bllossom-Vision v1.0, based on Bllossom\n* [2023/08] We released Bllossom v1.0, based on llama-2. \n* [2023/07] We released Bllossom v0.7, based on polyglot-ko.\n\n\n## Example code\n\n```python\n!CMAKE_ARGS=\"-DLLAMA_CUDA=on\" pip install llama-cpp-python\n!huggingface-cli download Bllossom/llama-3-Korean-Bllossom-70B-gguf-Q4_K_M --local-dir='YOUR-LOCAL-FOLDER-PATH'\n\nfrom llama_cpp import Llama\nfrom transformers import AutoTokenizer\n\nmodel_id = 'Bllossom/llama-3-Korean-Bllossom-70B-gguf-Q4_K_M'\ntokenizer = AutoTokenizer.from_pretrained(model_id)\nmodel = Llama(\n    model_path='YOUR-LOCAL-FOLDER-PATH/llama-3-Korean-Bllossom-70B-gguf-Q4_K_M.gguf',\n    n_ctx=512,\n    n_gpu_layers=-1        # Number of model layers to offload to GPU\n)\n\nPROMPT = \\\n'''당신은 유용한 AI 어시스턴트입니다. 사용자의 질의에 대해 친절하고 정확하게 답변해야 합니다.\nYou are a helpful AI assistant, you'll need to answer users' queries in a friendly and accurate manner.'''\n\ninstruction = 'Your Instruction'\n\nmessages = [\n    {\"role\": \"system\", \"content\": f\"{PROMPT}\"},\n    {\"role\": \"user\", \"content\": f\"{instruction}\"}\n    ]\n\nprompt = tokenizer.apply_chat_template(\n    messages, \n    tokenize = False,\n    add_generation_prompt=True\n)\n\ngeneration_kwargs = {\n    \"max_tokens\":512,\n    \"stop\":[\"<|eot_id|>\"],\n    \"echo\":True, # Echo the prompt in the output\n    \"top_p\":0.9,\n    \"temperature\":0.6,\n}\n\nresonse_msg = model(prompt, **generation_kwargs)\nprint(resonse_msg['choices'][0]['text'][len(prompt):])\n```\n\n\n\n## Citation\n**Language Model**\n```text\n@misc{bllossom,\n  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},\n  title = {Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean},\n  year = {2024},\n  journal = {LREC-COLING 2024},\n  paperLink = {\\url{https://arxiv.org/pdf/2403.10882}},\n },\n}\n```\n\n**Vision-Language Model**\n```text\n@misc{bllossom-V,\n  author = {Dongjae Shin, Hyunseok Lim, Inho Won, Changsu Choi, Minjun Kim, Seungwoo Song, Hangyeol Yoo, Sangmin Kim, Kyungtae Lim},\n  title = {X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment},\n  year = {2024},\n  publisher = {GitHub},\n  journal = {NAACL 2024 findings},\n  paperLink = {\\url{https://arxiv.org/pdf/2403.11399}},\n },\n}\n```\n\n## Contact\n - 임경태(KyungTae Lim), Professor at Seoultech. `ktlim@seoultech.ac.kr`\n - 함영균(Younggyun Hahm), CEO of Teddysum. `hahmyg@teddysum.ai`\n - 김한샘(Hansaem Kim), Professor at Yonsei. `khss@yonsei.ac.kr`\n\n## Contributor\n - 최창수(Chansu Choi), choics2623@seoultech.ac.kr\n - 김상민(Sangmin Kim), sangmin9708@naver.com\n - 원인호(Inho Won), wih1226@seoultech.ac.kr\n - 김민준(Minjun Kim), mjkmain@seoultech.ac.kr \n - 송승우(Seungwoo Song), sswoo@seoultech.ac.kr\n - 신동재(Dongjae Shin), dylan1998@seoultech.ac.kr\n - 임현석(Hyeonseok Lim), gustjrantk@seoultech.ac.kr\n - 육정훈(Jeonghun Yuk), usually670@gmail.com\n - 유한결(Hangyeol Yoo), 21102372@seoultech.ac.kr\n - 송서현(Seohyun Song), alexalex225225@gmail.com",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "llama-cpp",
    "gguf-my-repo",
    "en",
    "ko",
    "arxiv:2403.10882",
    "arxiv:2403.11399",
    "base_model:Bllossom/llama-3-Korean-Bllossom-70B",
    "base_model:quantized:Bllossom/llama-3-Korean-Bllossom-70B",
    "license:llama3",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
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  "last_modified": "2024-05-14T11:09:15.000Z",
  "created_at": "2024-05-08T16:10:28.000Z",
  "pipeline_tag": "",
  "library_name": "transformers"
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