Abeja

gpt-neox-japanese-2.7b

abeja

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gpt-neox-japanese-2.7b is a 2.7 billion parameter Japanese language model developed by ABEJA, Inc. based on the GPT-NeoX architecture. It was trained on a large corpus of Japanese text data including the Japanese CC-100, Japanese C4, and Japanese Wikipedia datasets. This model is part of a series of Japanese GPT-NeoX models released by ABEJA, with variants ranging from 1.3 billion to 2.7 billion parameters. The weblab-10b model is a 10 billion parameter Japanese-centric multilingual GPT-NeoX model developed by Matsuo Lab. It was trained on around 600 billion tokens from a mixture of Japanese C4 and The Pile datasets. Matsuo Lab has also released various finetuned versions of the weblab-10b model for instruction-following tasks. The gpt2 model is the smallest version of the GPT-2 language model, with 124 million parameters. It was trained on a large corpus of English web data and can be used for a variety of text generation tasks, though it may exhibit biases present in the training data. The japanese-gpt-neox-3.6b model is a 3.6 billion parameter Japanese GPT-NeoX model developed by Rinna. It was trained on over 312.5 billion tokens of Japanese text data from sources like Japanese CC-100, Japanese C4, and Japanese Wikipedia. Rinna has also released finetuned versions of this model for instruction-following tasks. Model inputs and outputs Inputs Text prompts**: The models take in text prompts as input, which they use to generate additional text. Outputs Generated text**: The models output generated text that continues or expands upon the provided prompt. The generated text can be of variable length depending on the specific use case. Capabilities The GPT-NeoX based models (gpt-neox-japanese-2.7b, weblab-10b, japanese-gpt-neox-3.6b) are strong at generating coherent and fluent Japanese text. They can be used for a variety of Japanese language tasks such as translation, summarization, question answering, and creative writing. The gpt2 model, while focused on English, can also be used for general text generation tasks. These models excel at tasks that involve producing human-like text, but they do not have strong reasoning capabilities and may incorporate biases present in their training data. It is important to carefully evaluate model outputs for accuracy and appropriateness before deploying them in production systems. What can I use it for? These models can be used for a wide range of Japanese language applications, such as: Content generation**: Generating articles, stories, poems, and other types of Japanese text. Language modeling**: Evaluating the grammatical and semantic coherence of Japanese text. Text summarization**: Summarizing longer Japanese documents into more concise versions. Machine translation**: Translating between Japanese and other languages. Chatbots and virtual assistants**: Powering conversational interfaces that can engage in natural language interaction in Japanese. The weblab-10b-instruction-sft model in particular is well-suited for instruction-following tasks, where the model can be prompted to perform specific actions or provide responses to open-ended prompts. Things to try Some interesting things to explore with these models include: Prompt engineering**: Experimenting with different prompts to see how the models respond and generate diverse outputs. Finetuning**: Further training the models on domain-specific data to improve performance on specialized tasks. Comparative analysis**: Comparing the outputs of the different models to understand their unique strengths and limitations. Bias analysis**: Evaluating the models for potential biases and developing strategies to mitigate them. Multimodal integration**: Combining these language models with other AI systems, such as vision or speech models, to enable more comprehensive applications. By experimenting with these models and exploring their capabilities, you can unlock a wide range of possibilities for Japanese language AI applications.

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