Matsuo-lab

Models by this creator

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weblab-10b-instruction-sft

matsuo-lab

Total Score

71

The weblab-10b-instruction-sft is a Japanese-centric multilingual GPT-NeoX model with 10 billion parameters. Trained using code based on EleutherAI/gpt-neox, it has a 36-layer, 4864-hidden-size transformer architecture. The model was pre-trained on around 600B tokens from a mixture of the Japanese C4 and The Pile datasets. It was then finetuned on a subset of records from datasets like Alpaca (English), Alpaca (Japanese translation), and others to serve as an instruction-following conversational agent. This model can be contrasted with the japanese-gpt-neox-3.6b-instruction-sft model, which is a 3.6 billion parameter Japanese GPT-NeoX model that has also been finetuned for instruction following. The key differences are the larger parameter size and broader pre-training dataset of the weblab-10b-instruction-sft model. Model inputs and outputs Inputs Text prompts**: The model takes in text prompts, which can include multi-turn conversations or instructions for the model to follow. Outputs Generated text**: The model outputs generated text that continues or responds to the provided prompt. This can include generating coherent, contextual responses to instructions or conversational prompts. Capabilities The weblab-10b-instruction-sft model can be used for a variety of language generation and understanding tasks, particularly ones involving Japanese. It demonstrates strong performance on the JGLUE 8-task evaluation, achieving high accuracy on tasks like JCommonsenseQA, JNLI, and MARC-ja. The model's large size and broad training data allow it to generate fluent, contextual responses to open-ended prompts, making it suitable for applications like chatbots and language assistants. What can I use it for? The weblab-10b-instruction-sft model could be a good starting point for building Japanese-language chatbots, virtual assistants, or other applications that require fluent text generation and language understanding. Its multilingual capabilities also allow it to potentially be used for cross-lingual applications. However, as with any large language model, it's important to carefully curate and filter the model's outputs to ensure safety and mitigate potential biases or inaccuracies. Things to try One interesting aspect of the weblab-10b-instruction-sft model is its ability to follow instructions and engage in open-ended dialogue. Prompts that involve multi-turn conversations or provide specific tasks or objectives for the model to complete could be a productive area to explore, leveraging the model's strong performance on the JGLUE benchmarks. Experimenting with different prompting techniques and finetuning approaches may also help unlock the model's full potential for downstream applications.

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Updated 5/21/2024

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weblab-10b

matsuo-lab

Total Score

62

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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Updated 5/21/2024