gpt-neox-japanese-2.7b
Maintainer: abeja
54
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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The weblab-10b is a Japanese-centric multilingual GPT-NeoX model with 10 billion parameters, developed by matsuo-lab. It was trained on a mixture of the Japanese C4 and The Pile datasets, totaling around 600 billion tokens. The model architecture consists of 36 layers and a 4864-hidden size, making it a large and powerful language model. Similar models in the series include the weblab-10b-instruction-sft variant, which has been fine-tuned for instruction-following. Model inputs and outputs The weblab-10b model takes in text as input and generates text as output, making it a versatile text-to-text language model. It can be used for a variety of natural language processing tasks, such as text generation, language understanding, and language translation. Inputs Text prompt: The model accepts arbitrary text as input, which it then uses to generate additional text. Outputs Generated text: The model outputs generated text that continues or responds to the input prompt. The length and content of the output can be controlled through various generation parameters. Capabilities The weblab-10b model has demonstrated strong performance on a range of Japanese language tasks, including commonsense question answering, natural language inference, and summarization. Its large scale and multilingual nature make it a powerful tool for working with Japanese language data. What can I use it for? The weblab-10b model can be used for a variety of applications, such as: Text generation**: The model can be used to generate coherent and context-appropriate Japanese text, which can be useful for tasks like creative writing, dialogue generation, or report summarization. Language understanding**: By fine-tuning the model on specific tasks, it can be used to improve performance on a range of Japanese NLP tasks, such as question answering or text classification. Multilingual applications**: The model's multilingual capabilities can be leveraged for applications that require translation or cross-lingual understanding. Things to try One interesting aspect of the weblab-10b model is its strong performance on Japanese language tasks, which highlights its potential for working with Japanese data. Researchers and developers could explore fine-tuning the model on domain-specific Japanese datasets to tackle specialized problems, or investigating its ability to generate coherent and contextually appropriate Japanese text. Another area to explore is the model's multilingual capabilities and how they can be leveraged for cross-lingual applications. Experiments could involve testing the model's ability to understand and generate text in multiple languages, or exploring zero-shot or few-shot learning approaches for tasks like machine translation. Overall, the weblab-10b model represents a powerful and flexible language model that can be a valuable tool for a wide range of Japanese and multilingual NLP applications.
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The japanese-gpt-neox-3.6b is a 3.6 billion parameter Japanese language model developed by rinna. The model was trained using the EleutherAI/gpt-neox codebase on a dataset of over 312.5 billion Japanese tokens from sources like Japanese CC-100, Japanese C4, and Japanese Wikipedia. This results in a model with a validation perplexity of 8.68. The model comes in several variants, including an instruction-following fine-tuned version (rinna/japanese-gpt-neox-3.6b-instruction-sft) and a reinforcement learning version (rinna/japanese-gpt-neox-3.6b-instruction-ppo). These variants allow the model to better understand and follow human instructions. In comparison, the gpt-neox-20b model is a 20 billion parameter English language model trained by EleutherAI, while the mGPT model is a 1.3 billion parameter multilingual model developed by AI-Forever covering 61 languages. The gpt-j-6b model is a 6 billion parameter English language model developed by EleutherAI. Model Inputs and Outputs Inputs Text prompts in Japanese for the model to continue and generate additional text. Outputs Continued Japanese text generated by the model based on the input prompt. Capabilities The japanese-gpt-neox-3.6b model can be used for a variety of Japanese language tasks, such as text generation, summarization, translation, and question answering. The model's strong performance on the Japanese language corpus allows it to generate coherent and contextually relevant Japanese text. The fine-tuned variants of the model, like rinna/japanese-gpt-neox-3.6b-instruction-sft, demonstrate an even stronger ability to understand and follow human instructions, making them useful for building interactive Japanese language assistants or chatbots. What Can I Use It For? The japanese-gpt-neox-3.6b model can be a valuable tool for Japanese language researchers and developers. It can be used as a base model for fine-tuning on specific Japanese language tasks, or as a starting point for developing personalized Japanese language applications. For example, a Japanese language tutoring app could use the model to generate natural Japanese responses to student prompts, providing an immersive language learning experience. Alternatively, a Japanese e-commerce platform could leverage the model's text generation capabilities to automatically produce product descriptions and summaries. The instruction-following variants of the model, like rinna/japanese-gpt-neox-3.6b-instruction-sft, could be used to build sophisticated Japanese language assistants that can understand and execute complex user requests. Things to Try One interesting aspect of the japanese-gpt-neox-3.6b model is its ability to generate coherent and contextually relevant Japanese text. Try providing the model with a Japanese sentence or paragraph as a prompt and see how it continues the text. Observe how the model maintains the style, tone, and overall coherence of the generated output. You can also experiment with the different variants of the model, like rinna/japanese-gpt-neox-3.6b-instruction-sft, and compare their performance on tasks that require understanding and following human instructions. This can give you insights into the model's robustness and potential applications.
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