Rakuten
Models by this creator
🎯
RakutenAI-7B-chat
51
RakutenAI-7B-chat is a Japanese language model developed by Rakuten. It builds upon the Mistral model architecture and the Mistral-7B-v0.1 pre-trained checkpoint. Rakuten has extended the vocabulary from 32k to 48k to improve the character-per-token rate for Japanese. According to an independent evaluation by Kamata et al., the instruction-tuned and chat versions of RakutenAI-7B achieve the highest performance among similar models like OpenCalm, Elyza, Youri, Nekomata and Swallow on Japanese language benchmarks. Model inputs and outputs Inputs Text prompts provided to the model in the form of a conversational exchange between a user and an AI assistant. Outputs Responses generated by the model to continue the conversation in a helpful and polite manner. Capabilities RakutenAI-7B-chat is capable of engaging in open-ended conversations and providing detailed, informative responses on a wide range of topics. Its strong performance on Japanese language benchmarks suggests it can understand and generate high-quality Japanese text. What can I use it for? RakutenAI-7B-chat could be used to power conversational AI assistants for Japanese-speaking users, providing helpful information and recommendations on various subjects. Developers could integrate it into chatbots, virtual agents, or other applications that require natural language interaction in Japanese. Things to try With RakutenAI-7B-chat, you can experiment with different types of conversational prompts to see how the model responds. Try asking it for step-by-step instructions, opinions on current events, or open-ended questions about its own capabilities. The model's strong performance on Japanese benchmarks suggests it could be a valuable tool for a variety of Japanese language applications.
Updated 6/13/2024
🎯
RakutenAI-7B-chat
51
RakutenAI-7B-chat is a Japanese language model developed by Rakuten. It builds upon the Mistral model architecture and the Mistral-7B-v0.1 pre-trained checkpoint. Rakuten has extended the vocabulary from 32k to 48k to improve the character-per-token rate for Japanese. According to an independent evaluation by Kamata et al., the instruction-tuned and chat versions of RakutenAI-7B achieve the highest performance among similar models like OpenCalm, Elyza, Youri, Nekomata and Swallow on Japanese language benchmarks. Model inputs and outputs Inputs Text prompts provided to the model in the form of a conversational exchange between a user and an AI assistant. Outputs Responses generated by the model to continue the conversation in a helpful and polite manner. Capabilities RakutenAI-7B-chat is capable of engaging in open-ended conversations and providing detailed, informative responses on a wide range of topics. Its strong performance on Japanese language benchmarks suggests it can understand and generate high-quality Japanese text. What can I use it for? RakutenAI-7B-chat could be used to power conversational AI assistants for Japanese-speaking users, providing helpful information and recommendations on various subjects. Developers could integrate it into chatbots, virtual agents, or other applications that require natural language interaction in Japanese. Things to try With RakutenAI-7B-chat, you can experiment with different types of conversational prompts to see how the model responds. Try asking it for step-by-step instructions, opinions on current events, or open-ended questions about its own capabilities. The model's strong performance on Japanese benchmarks suggests it could be a valuable tool for a variety of Japanese language applications.
Updated 6/13/2024
🚀
RakutenAI-7B
42
The RakutenAI-7B model is a large language model developed by Rakuten that achieves strong performance on Japanese language understanding benchmarks while also performing competitively on English test sets. It leverages the Mistral model architecture and is based on the Mistral-7B-v0.1 pre-trained checkpoint, exemplifying a successful retrofitting of the pre-trained model weights. The model also extends Mistral's vocabulary from 32k to 48k to offer better character-per-token rate for Japanese. According to the provided benchmarks, RakutenAI-7B outperforms similar models like OpenCalm, Elyza, Youri, Nekomata, and Swallow on several Japanese language understanding tasks. Model Inputs and Outputs Inputs The model accepts text input in Japanese and English. Outputs The model generates human-like text in Japanese and English. Capabilities The RakutenAI-7B model demonstrates strong performance on a variety of Japanese language understanding tasks, including JSNLI, RTE, KUCI, JCS, and JNLI. It also maintains competitive results on English test sets compared to similar models. Rakuten has further fine-tuned the foundation model to create the RakutenAI-7B-instruct and RakutenAI-7B-chat models for specific use cases. What Can I Use It For? The RakutenAI-7B model can be used for a variety of natural language processing tasks, such as text generation, language understanding, and translation between Japanese and English. Its strong performance on Japanese benchmarks makes it well-suited for applications targeting the Japanese market, such as customer service chatbots, content generation, and language learning tools. Rakuten has also made available the RakutenAI-7B-instruct and RakutenAI-7B-chat models, which can be used for instruction-following and open-ended conversational tasks, respectively. Things to Try One interesting aspect of the RakutenAI-7B model is its ability to perform well on both Japanese and English tasks, making it a versatile model for multilingual applications. Developers could explore using the model for tasks that require understanding and generation in both languages, such as translation, cross-lingual information retrieval, or even building language learning tools that can adapt to the user's native language. Another area to explore is the model's performance on various Japanese-specific tasks, such as sentiment analysis, text summarization, or question answering on Japanese-language data. Leveraging the model's strong performance on Japanese benchmarks could lead to interesting applications tailored to the Japanese market.
Updated 9/6/2024