weblab-10b-instruction-sft
Maintainer: matsuo-lab
72
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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
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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