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Yanolja

Models by this creator

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EEVE-Korean-Instruct-10.8B-v1.0

yanolja

Total Score

70

EEVE-Korean-Instruct-10.8B-v1.0 is a fine-tuned version of yanolja/EEVE-Korean-10.8B-v1.0, which is a Korean vocabulary-extended version of upstage/SOLAR-10.7B-v1.0. The model was fine-tuned using Direct Preference Optimization (DPO) through the use of Axolotl. This approach aims to efficiently expand the vocabulary for large language models to better handle the Korean language. Model inputs and outputs Inputs Prompts**: The model expects natural language prompts in Korean as input. Outputs Text**: The model generates coherent, fluent Korean text in response to the given prompts. Capabilities EEVE-Korean-Instruct-10.8B-v1.0 is capable of engaging in open-ended dialogue, answering questions, and generating text on a wide range of topics in the Korean language. The model's fine-tuning has aimed to improve its ability to provide helpful, detailed, and polite responses. What can I use it for? This model can be used for a variety of Korean language tasks, such as chatbots, language generation, question answering, and text summarization. It could be particularly useful for developers and researchers working on Korean-language AI applications that require natural language understanding and generation. Things to try You can experiment with providing the model with different types of prompts in Korean to see the range of outputs it can generate. Try asking it questions, giving it open-ended prompts, or requesting it to complete specific tasks. Pay attention to the coherence, fluency, and helpfulness of the model's responses.

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Updated 5/17/2024

👀

EEVE-Korean-10.8B-v1.0

yanolja

Total Score

50

EEVE-Korean-10.8B-v1.0 is a large language model developed by yanolja that extends the capabilities of the upstage/SOLAR-10.7B-v1.0 model to handle Korean language tasks. The model was fine-tuned on various Korean web-crawled datasets to expand its vocabulary and understanding of the Korean language. The team at yanolja used a multi-stage training approach to efficiently integrate the new Korean vocabulary into the base model, preserving the original parameter structure while expanding the model's cross-linguistic abilities. This method focuses on gradually training from input embeddings to full parameters, allowing the model to leverage the inherent capabilities of the foundational English-based model. Model inputs and outputs Inputs The EEVE-Korean-10.8B-v1.0 model accepts text input in the Korean language. Outputs The model generates text in Korean, providing responses to prompts or engaging in open-ended conversations. Capabilities The EEVE-Korean-10.8B-v1.0 model is capable of performing a wide range of Korean language tasks, including natural language generation, question answering, and text summarization. The model's expanded vocabulary and fine-tuning on Korean datasets allow it to better understand and generate coherent, natural-sounding Korean text. What can I use it for? The EEVE-Korean-10.8B-v1.0 model can be utilized in a variety of applications that require Korean language processing, such as: Building conversational AI assistants to interact with Korean-speaking users Generating Korean-language content for blogs, articles, or creative writing Summarizing Korean-language text from news articles, social media, or other sources Providing Korean-language question answering capabilities for educational or informational applications Things to try One interesting aspect of the EEVE-Korean-10.8B-v1.0 model is its ability to blend the capabilities of a foundational English-based model with the nuances of the Korean language. Developers could experiment with prompts that require both linguistic and cross-cultural understanding, such as generating Korean-language narratives inspired by traditional Korean folktales or providing Korean-language explanations of complex technical concepts. Additionally, the model's efficient vocabulary expansion approach could serve as a template for adapting other large language models to new languages, potentially unlocking access to a wider range of linguistic resources for AI applications.

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Updated 5/17/2024