# WizardMath-70B-V1.0

Maintainer: WizardLMTeam

116

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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 `WizardMath-70B-V1.0`

model is a large language model developed by the WizardLM team that is focused on empowering mathematical reasoning capabilities. It was trained using a novel method called Reinforced Evol-Instruct (RLEIF), which involves automatically generating a diverse set of math-related instructions to fine-tune the model.

The model is one of several in the WizardMath series, which also includes smaller 13B and 7B versions. Compared to other open-source math LLMs, the WizardMath-70B-V1.0 model significantly outperforms on key benchmarks like the GSM8k and MATH datasets, achieving 81.6 pass@1 and 22.7 pass@1 respectively. This puts it ahead of state-of-the-art models like ChatGPT 3.5, Claude Instant 1, and PaLM 2 540B.

## Model inputs and outputs

### Inputs

**Natural language instructions**: The model takes in text-based instructions or prompts related to math problems or reasoning tasks.

### Outputs

**Textual responses**: The model generates text-based responses that attempt to solve the given math problem or provide a reasoned answer.

## Capabilities

The `WizardMath-70B-V1.0`

model demonstrates strong capabilities in mathematical reasoning and problem-solving. It can tackle a wide range of math-related tasks, from simple arithmetic to more complex algebra, geometry, and even calculus problems. The model is particularly adept at step-by-step reasoning, clearly explaining its thought process and showing its work.

## What can I use it for?

The `WizardMath-70B-V1.0`

model could be useful for a variety of applications that require advanced mathematical skills, such as:

- Providing homework help and tutoring for students struggling with math
- Automating the generation of math practice problems and solutions
- Integrating math reasoning capabilities into educational apps and games
- Aiding in the development of math-focused AI assistants

## Things to try

One interesting aspect of the `WizardMath-70B-V1.0`

model is its ability to handle multi-step math problems. Try providing it with complex word problems or story-based math questions and see how it breaks down the problem and arrives at the solution. You can also experiment with prompting the model to explain its reasoning in more detail or to explore alternative solution approaches.

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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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.

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### WizardMath-7B-V1.1

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The WizardMath-7B-V1.1 is a powerful 7B parameter language model developed by the WizardLM Team that has been trained to excel at mathematical reasoning and problem-solving tasks. It was initialized from the Mistral-7B model, which is considered the state-of-the-art 7B math language model, and has been further refined using the Reinforced Evol-Instruct (RLEIF) technique. The WizardMath-7B-V1.1 model outperforms other open-source 7B-sized math language models like Llama 1-7B, Llama 2-7B, and Yi-6b on the GSM8K and MATH benchmarks. It even surpasses the performance of larger models like ChatGPT 3.5, Gemini Pro, and Mixtral MOE in certain areas. This makes the WizardMath-7B-V1.1 an excellent choice for applications that require advanced mathematical reasoning capabilities. Model inputs and outputs Inputs The model accepts natural language text as input, such as math-related prompts or questions. Outputs The model generates natural language responses that demonstrate its ability to solve mathematical problems and reason about quantitative concepts. Capabilities The WizardMath-7B-V1.1 model has been specifically trained to excel at mathematical reasoning and problem-solving tasks. It can tackle a wide range of math-related problems, from simple arithmetic to complex algebraic and geometric concepts. The model is particularly adept at generating step-by-step solutions and explanations, making it a valuable tool for educational and tutorial applications. What can I use it for? The WizardMath-7B-V1.1 model can be used for a variety of applications that require advanced mathematical reasoning capabilities. Some potential use cases include: Educational tools**: The model can be integrated into educational platforms to provide interactive math tutoring, answer questions, and generate personalized learning materials. Research and analysis**: Researchers and analysts can leverage the model's capabilities to automate and streamline mathematical problem-solving and data analysis tasks. Business and finance**: The model can be used to assist with financial modeling, risk analysis, and other quantitative business applications. AI-powered chatbots and virtual assistants**: The WizardMath-7B-V1.1 model can be incorporated into chatbots and virtual assistants to provide users with math-related support and problem-solving assistance. Things to try One interesting aspect of the WizardMath-7B-V1.1 model is its ability to provide step-by-step explanations for its reasoning and problem-solving process. Try posing the model with complex math problems and observe how it breaks down the problem, applies relevant mathematical concepts, and arrives at the final solution. This can provide valuable insights into the model's understanding of mathematical reasoning and potentially help users improve their own problem-solving skills. Another interesting experiment would be to compare the performance of the WizardMath-7B-V1.1 model with other math-focused language models, such as the Llemma 7B model from EleutherAI. This could help you better understand the unique strengths and limitations of each model, and inform your choice of the most suitable option for your specific use case.

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WizardLM-70B-V1.0 is a large language model developed by the WizardLM Team. It is part of the WizardLM family of models, which also includes the WizardCoder and WizardMath models. The WizardLM-70B-V1.0 model was trained to follow complex instructions and demonstrates strong performance on tasks like open-ended conversation, reasoning, and math problem-solving. Compared to similar large language models, the WizardLM-70B-V1.0 exhibits several key capabilities. It outperforms some closed-source models like ChatGPT 3.5, Claude Instant 1, and PaLM 2 540B on the GSM8K benchmark, achieving an 81.6 pass@1 score, which is 24.8 points higher than the current SOTA open-source LLM. Additionally, the model achieves a 22.7 pass@1 score on the MATH benchmark, 9.2 points above the SOTA open-source LLM. Model inputs and outputs Inputs Natural language instructions and prompts**: The model is designed to accept a wide range of natural language inputs, from open-ended conversation to specific task descriptions. Outputs Natural language responses**: The model generates coherent and contextually appropriate responses to the given inputs. This can include answers to questions, elaborations on ideas, and solutions to problems. Code generation**: The WizardLM-70B-V1.0 model has also been shown to excel at code generation, with its WizardCoder variant achieving state-of-the-art performance on benchmarks like HumanEval. Capabilities The WizardLM-70B-V1.0 model demonstrates impressive capabilities across a range of tasks. It is able to engage in open-ended conversation, providing helpful and detailed responses. The model also excels at reasoning and problem-solving, as evidenced by its strong performance on the GSM8K and MATH benchmarks. One key strength of the WizardLM-70B-V1.0 is its ability to follow complex instructions and tackle multi-step problems. Unlike some language models that struggle with tasks requiring sequential reasoning, this model is able to break down instructions, generate relevant outputs, and provide step-by-step solutions. What can I use it for? The WizardLM-70B-V1.0 model has a wide range of potential applications. It could be used to power conversational AI assistants, provide tutoring and educational support, assist with research and analysis tasks, or even help with creative writing and ideation. The model's strong performance on math and coding tasks also makes it well-suited for use in STEM education, programming tools, and scientific computing applications. Developers could leverage the WizardCoder variant to build intelligent code generation and autocomplete tools. Things to try One interesting aspect of the WizardLM-70B-V1.0 model is its ability to engage in multi-turn conversations and follow up on previous context. Try providing the model with a series of related prompts and see how it maintains coherence and builds upon the discussion. You could also experiment with the model's reasoning and problem-solving capabilities by presenting it with complex, multi-step instructions or math problems. Observe how the model breaks down the task, generates intermediate steps, and arrives at a final solution. Another area to explore is the model's versatility across different domains. Test its performance on a variety of tasks, from open-ended conversation to specialized technical queries, to understand the breadth of its capabilities.

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### WizardCoder-15B-V1.0

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The WizardCoder-15B-V1.0 model is a large language model (LLM) developed by the WizardLM Team that has been fine-tuned specifically for coding tasks using their Evol-Instruct method. This method involves automatically generating a diverse set of code-related instructions to further train the model on instruction-following capabilities. Compared to similar open-source models like CodeGen-16B-Multi, LLaMA-33B, and StarCoder-15B, the WizardCoder-15B-V1.0 model exhibits significantly higher performance on the HumanEval benchmark, achieving a pass@1 score of 57.3 compared to the 18.3-37.8 range of the other models. Model inputs and outputs Inputs Natural language instructions**: The model takes in natural language prompts that describe coding tasks or problems to be solved. Outputs Generated code**: The model outputs code in a variety of programming languages (e.g. Python, Java, etc.) that attempts to solve the given problem or complete the requested task. Capabilities The WizardCoder-15B-V1.0 model has been specifically trained to excel at following code-related instructions and generating functional code to solve a wide range of programming problems. It is capable of tasks such as writing simple algorithms, fixing bugs in existing code, and even generating complex programs from high-level descriptions. What can I use it for? The WizardCoder-15B-V1.0 model could be a valuable tool for developers, students, and anyone working on code-related projects. Some potential use cases include: Prototyping and rapid development of new software features Automating repetitive coding tasks Helping to explain programming concepts by generating sample code Tutoring and teaching programming by providing step-by-step solutions Things to try One interesting thing to try with the WizardCoder-15B-V1.0 model is to provide it with vague or open-ended prompts and see how it interprets and responds to them. For example, you could ask it to "Write a Python program that analyzes stock market data" and see the creative and functional solutions it comes up with. Another idea is to give the model increasingly complex or challenging coding problems, like those found on programming challenge websites, and test its ability to solve them. This can help uncover the model's strengths and limitations when it comes to more advanced programming tasks.

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