Beomi
Models by this creator
🎯
llama-2-ko-7b
169
The llama-2-ko-7b model is an advanced iteration of the Llama 2 language model, developed by Junbum Lee (Beomi). This model builds upon the capabilities of Llama 2 by incorporating a Korean corpus into its further pretraining, resulting in an expanded vocabulary and improved performance on Korean-language tasks. Like Llama 2, llama-2-ko-7b operates within the 7 billion parameter range of the Llama 2 family of models. Model inputs and outputs Inputs Text**: The llama-2-ko-7b model takes text as input. Outputs Text**: The model generates text as output. Capabilities The llama-2-ko-7b model is a powerful generative language model that can be leveraged for a variety of Korean-language tasks. Its expanded vocabulary and Korean-specific pretraining allow it to generate more natural and contextually-relevant text compared to models trained solely on English data. This makes it a compelling option for applications such as chatbots, content generation, and language translation involving the Korean language. What can I use it for? The llama-2-ko-7b model can be used for a range of Korean-language natural language processing tasks, including: Chatbots and conversational AI**: The model's ability to generate coherent and contextual Korean-language text makes it well-suited for building chatbots and other conversational AI assistants. Content generation**: llama-2-ko-7b can be used to generate Korean-language articles, product descriptions, and other types of content. Language translation**: The model's understanding of Korean language structure and vocabulary can be leveraged to assist in translating between Korean and other languages. Things to try One interesting aspect of the llama-2-ko-7b model is its handling of Korean tokenization. Compared to the original Llama 2 model, llama-2-ko-7b tokenizes Korean text in a more natural and intuitive way, treating punctuation marks like commas and periods as separate tokens. This can lead to more coherent and grammatically-correct text generation in Korean. Developers working on Korean-language NLP tasks may want to experiment with using llama-2-ko-7b as a starting point and fine-tuning it further on domain-specific data to unlock its full potential.
Updated 5/28/2024
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Llama-3-Open-Ko-8B
75
The Llama-3-Open-Ko-8B model is a continued pretrained language model based on the original Llama-3-8B. This model was trained fully on publicly available resources, including over 60GB of deduplicated texts. It uses the new Llama-3 tokenizer and was pretrained on 17.7B+ tokens, slightly more than the previous Llama-2-Ko tokenizer. The training was done on TPUv5e-256 with support from the TRC program by Google. The maintainer, Junbum Lee (Beomi), also released an instruction-tuned version called Llama-3-Open-Ko-8B-Instruct-preview. This model was trained using the idea from the Chat Vector paper and serves as a starting point for creating new chat/instruct models. Compared to the previous Llama-2-Ko-7b model, the Llama-3-Open-Ko-8B has a larger vocabulary size of 46,336 and improved tokenization for Korean text. Model inputs and outputs Inputs Text**: The model takes text as input. Outputs Text**: The model generates text as output. Code**: The model can also generate code. Capabilities The Llama-3-Open-Ko-8B model can be used for a variety of natural language processing tasks, including text generation, language modeling, and code generation. Its expanded vocabulary and improved tokenization for Korean text make it a more capable model for working with Korean language data compared to the previous Llama-2-Ko-7b. The instruction-tuned Llama-3-Open-Ko-8B-Instruct-preview model is particularly well-suited for chatbot and assistant-like applications, as it has been optimized for dialog use cases. What can I use it for? The Llama-3-Open-Ko-8B and Llama-3-Open-Ko-8B-Instruct-preview models can be used for a range of commercial and research applications involving Korean text and language generation, such as: Text generation**: Generating high-quality Korean text for content creation, summarization, and creative writing. Chatbots and assistants**: Building conversational AI assistants that can engage in natural dialog in Korean. Code generation**: Generating Korean-language code snippets or entire programs. Language modeling**: Pretraining on the Llama-3-Open-Ko-8B model and fine-tuning for Korean-specific NLP tasks. Things to try One interesting aspect of the Llama-3-Open-Ko-8B model is its improved tokenization for Korean text compared to the previous Llama-2-Ko model. You could experiment with the model's ability to handle Korean language input and output, and compare its performance to other Korean language models. Additionally, the instruction-tuned Llama-3-Open-Ko-8B-Instruct-preview model provides a good starting point for building more advanced Korean chatbots and assistants.
Updated 5/30/2024
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KoAlpaca-Polyglot-5.8B
55
The KoAlpaca-Polyglot-5.8B is a fine-tuned version of the EleutherAI/polyglot-ko-5.8b model on a Korean language dataset. It was developed by beomi, who has a strong background in natural language processing. The model is similar to other large Korean language models like polyglot-ko-5.8b and llama-2-ko-7b, but it has been further fine-tuned on a Korean-specific dataset. Model inputs and outputs Inputs The model takes in text as input. Outputs The model generates text as output, making it well-suited for tasks like language generation, translation, and text summarization. Capabilities The KoAlpaca-Polyglot-5.8B model has been trained on a large corpus of Korean language data, giving it strong capabilities in understanding and generating high-quality Korean text. It can be used for a variety of Korean language tasks, including answering questions, generating coherent paragraphs, and translating between Korean and other languages. What can I use it for? The KoAlpaca-Polyglot-5.8B model can be used for a wide range of Korean language applications, such as building chatbots, writing assistants, and language learning tools. Its strong performance on benchmarks like KOBEST suggests it could also be useful for more advanced tasks like question-answering and text summarization. Things to try One interesting aspect of the KoAlpaca-Polyglot-5.8B model is its ability to handle sensitive information in the training data, such as personal identifiers. This suggests that the model could be used to build applications that need to handle private user data in a responsible way. Researchers and developers could explore using the model as a starting point for building custom Korean language models tailored to specific use cases or domains.
Updated 5/28/2024
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OPEN-SOLAR-KO-10.7B
54
The OPEN-SOLAR-KO-10.7B model is an advanced iteration of the previous upstage/SOLAR-10.7B-v1.0 model, featuring an expanded vocabulary and the inclusion of a Korean corpus for enhanced pretraining. This model was developed by Junbum Lee (Beomi), a model maintainer on Hugging Face. Compared to the original SOLAR model, the OPEN-SOLAR-KO-10.7B version has a larger vocabulary size of 46,592, and it utilizes a more efficient tokenization process that reduces the number of tokens required for certain commonly used Korean text. This allows the model to handle Korean text more effectively. The training data for OPEN-SOLAR-KO-10.7B comes exclusively from publicly accessible Korean corpora, including sources such as AI Hub, Modu Corpus, and Korean Wikipedia. By using only publicly available data, this model is open for unrestricted use by everyone, adhering to the Apache2.0 open-source license. Model inputs and outputs Inputs Text input only Outputs Text output only Capabilities The OPEN-SOLAR-KO-10.7B model is a powerful language model that can generate human-like text in Korean. It has demonstrated strong performance in various language tasks, such as text generation, summarization, and question answering. The model's expanded vocabulary and efficient tokenization allow it to handle Korean text with high accuracy. What can I use it for? The OPEN-SOLAR-KO-10.7B model is well-suited for a variety of Korean language-related applications, such as chatbots, content generation, language translation, and more. It can be fine-tuned on domain-specific data to create specialized models for tasks like customer service, education, or creative writing. Things to try One interesting aspect of the OPEN-SOLAR-KO-10.7B model is its use of publicly available data for pretraining. This approach allows for open and unrestricted use of the model, making it accessible to a wide range of developers and researchers. You could explore using this model as a starting point for your own Korean language projects, or fine-tune it on your own data to create a specialized model tailored to your needs.
Updated 6/26/2024
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Llama-3-Open-Ko-8B-Instruct-preview
51
The Llama-3-Open-Ko-8B-Instruct-preview model is a continuation of the Llama-3-8B model, developed by Junbum Lee (Beomi) and trained on over 60GB of publicly available Korean text data. This model uses the new Llama-3 tokenizer and was trained on 17.7B+ tokens, slightly more than the Llama-2-Ko tokenizer. The model was trained on TPUv5e-256 with support from the TRC program by Google. Applying ideas from the Chat Vector paper, the maintainer released an instruction-tuned version called the Llama-3-Open-Ko-8B-Instruct-preview, which serves as a great starting point for creating new chat and instruction models. Model inputs and outputs Inputs Text input only Outputs Generates text and code Capabilities The Llama-3-Open-Ko-8B-Instruct-preview model is a capable text generation model that can handle a variety of tasks, from open-ended conversation to code generation. Its Korean language understanding and generation abilities make it well-suited for applications targeting Korean-speaking users. What can I use it for? The Llama-3-Open-Ko-8B-Instruct-preview model can be used for a wide range of natural language processing tasks, such as chatbots, language modeling, and text generation. Its instruction-tuned version is particularly well-suited for building conversational AI assistants that can engage in open-ended dialogue and help with various tasks. Developers can fine-tune the model further using their own data to create custom applications tailored to their specific needs. Things to try One interesting aspect of the Llama-3-Open-Ko-8B-Instruct-preview model is its potential for multilingual applications. While the model was primarily trained on Korean data, its underlying architecture is capable of generating text in multiple languages. Developers could explore fine-tuning the model on datasets in other languages to create versatile language models that can serve users across different linguistic backgrounds.
Updated 7/18/2024
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KoAlpaca-Polyglot-12.8B
51
The KoAlpaca-Polyglot-12.8B model is a fine-tuned version of the EleutherAI/polyglot-ko-12.8b model on a KoAlpaca Dataset v1.1b. This large-scale Korean autoregressive language model was developed by the EleutherAI polyglot team, led by maintainer beomi. It is similar to other Polyglot-Ko models like the KoAlpaca-Polyglot-5.8B and polyglot-ko-12.8b, which were also trained on a large Korean dataset curated by TUNiB. Model inputs and outputs Inputs Text data Outputs Generates text Capabilities The KoAlpaca-Polyglot-12.8B model can be used for a variety of Korean language tasks, such as text generation, question answering, and sentiment analysis. It has shown strong performance on benchmarks like KOBEST, outperforming comparable models like skt/ko-gpt-trinity-1.2B-v0.5 and kakaobrain/kogpt. What can I use it for? The KoAlpaca-Polyglot-12.8B model could be used for projects that require Korean language generation or understanding, such as chatbots, content creation tools, or language learning applications. Given its strong performance on tasks like sentiment analysis, it could also be applied to analyzing Korean social media or customer feedback. As an open-source model, it provides a solid foundation for further fine-tuning or customization to meet specific needs. Things to try Developers could experiment with using the KoAlpaca-Polyglot-12.8B model for creative writing tasks, such as generating Korean poetry or short stories. The model's large scale and diverse training data may allow it to capture nuanced Korean language patterns and generate compelling, human-like text. Researchers could also further evaluate the model's robustness and limitations by testing it on a wider range of Korean language understanding benchmarks.
Updated 5/28/2024
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gemma-ko-7b
41
The gemma-ko-7b model is a 7B parameter version of the Gemma-Ko model, created by Junbum Lee (Beomi) and Taekyoon Choi. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. The original Gemma model is also available as a 7B base model and 2B base model. These models are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Model inputs and outputs Inputs Text string**: The model accepts a text string as input, such as a question, a prompt, or a document to be summarized. Outputs Generated text**: The model generates English-language text in response to the input, such as an answer to a question or a summary of a document. Capabilities The gemma-ko-7b model is well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Its relatively small size makes it possible to deploy in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state-of-the-art AI models and helping foster innovation. What can I use it for? The Gemma models have a wide range of potential use cases across various industries and domains. Some examples include: Content Creation and Communication**: Generating creative text formats such as poems, scripts, code, marketing copy, and email drafts; powering conversational interfaces for customer service, virtual assistants, or interactive applications; summarizing text corpuses, research papers, or reports. Research and Education**: Serving as a foundation for NLP research, developing algorithms, and contributing to the advancement of the field; supporting interactive language learning experiences, aiding in grammar correction or providing writing practice; assisting researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. Things to try One key aspect of the gemma-ko-7b model is its small size compared to other large language models, while still maintaining strong performance. This makes it a great choice for deployment in resource-constrained environments, such as on a laptop or desktop. You can experiment with running the model on different hardware setups to see how it performs and how it compares to other options. Additionally, the Gemma models are designed with a focus on responsible AI development, undergoing careful scrutiny and evaluation for safety, bias, and other ethical considerations. As you explore the capabilities of the gemma-ko-7b model, keep these principles in mind and consider ways to use the model responsibly within your own applications and use cases.
Updated 9/6/2024