WizardMath-7B-V1.0
Maintainer: WizardLMTeam
51
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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
WizardMath-7B-V1.0 is a large language model developed by the WizardLMTeam that aims to empower mathematical reasoning for large language models. It is part of the WizardLM series, which also includes WizardCoder and WizardMath models. The WizardMath-7B-V1.0 model was trained using the Reinforced Evol-Instruct (RLEIF) method, which aims to enhance the mathematical reasoning capabilities of large language models.
Model inputs and outputs
Inputs
- Text prompts: The model accepts natural language text prompts as input, which can contain mathematical questions, problems, or instructions.
Outputs
- Text responses: The model generates natural language text responses that aim to appropriately complete the requested mathematical task.
Capabilities
The WizardMath-7B-V1.0 model demonstrates strong performance on mathematical benchmarks, achieving a 54.9 pass@1 score on the GSM8k benchmark and a 10.7 pass@1 score on the MATH benchmark. This outperforms many other open-source 7B-sized math LLMs, such as MPT-7B, Llama 1-7B, and Llama 2-7B.
What can I use it for?
The WizardMath-7B-V1.0 model can be used for a variety of mathematical tasks, such as solving grade school math problems, performing numerical calculations, explaining mathematical concepts, and generating step-by-step solutions to complex math problems. This makes it a valuable tool for students, educators, researchers, and anyone who requires mathematical assistance.
Things to try
One interesting aspect of the WizardMath-7B-V1.0 model is its ability to provide step-by-step explanations for its solutions, which can be helpful for understanding the reasoning behind the answers. Users can experiment with providing the model with complex math problems and observe how it breaks down the problem and walks through the solution.
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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