deepseek-math-7b-instruct
Maintainer: deepseek-ai
68
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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
deepseek-math-7b-instruct is an AI model developed by DeepSeek AI that aims to push the limits of mathematical reasoning in open language models. It is an instruct-tuned version of the base deepseek-math-7b-base model, which was initialized with the deepseek-coder-7b-base-v1.5 model and then further pre-trained on math-related tokens from Common Crawl, along with natural language and code data.
The base model has achieved an impressive 51.7% score on the competition-level MATH benchmark, approaching the performance of Gemini-Ultra and GPT-4 without relying on external toolkits or voting techniques. The instruct model and the RL model built on top of the base model further improve its mathematical problem-solving capabilities.
Model inputs and outputs
Inputs
- text: The input text, which can be a mathematical question or problem statement. For example: "what is the integral of x^2 from 0 to 2? Please reason step by step, and put your final answer within \boxed{}."
- top_k: The number of highest probability vocabulary tokens to keep for top-k-filtering.
- top_p: If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
- temperature: The value used to modulate the next token probabilities.
- max_new_tokens: The maximum number of tokens to generate, ignoring the number of tokens in the prompt.
Outputs
The model generates a text response that provides a step-by-step solution and final answer to the input mathematical problem.
Capabilities
The deepseek-math-7b-instruct model is capable of solving a wide range of mathematical problems, from basic arithmetic to advanced calculus and linear algebra. It can provide detailed, step-by-step reasoning and solutions without relying on external tools or resources.
The model has also demonstrated strong performance on other benchmarks, such as natural language understanding, reasoning, and programming. It can be used for tasks like answering math-related questions, generating proofs and derivations, and even writing code to solve mathematical problems.
What can I use it for?
The deepseek-math-7b-instruct model can be useful for a variety of applications, including:
- Educational tools: The model can be integrated into educational platforms or tutoring systems to provide personalized, step-by-step math instruction and feedback to students.
- Research and academic work: Researchers and academics working in fields like mathematics, physics, or engineering can use the model to assist with problem-solving, proof generation, and other math-related tasks.
- Business and finance: The model can be used to automate the analysis of financial data, perform risk assessments, and support decision-making in various business domains.
- AI and ML development: The model's strong mathematical reasoning capabilities can be leveraged to build more robust and capable AI systems, particularly in domains that require advanced mathematical modeling and problem-solving.
Things to try
Some ideas for things to try with the deepseek-math-7b-instruct model include:
- Posing a variety of mathematical problems, from basic arithmetic to advanced calculus and linear algebra, and observing the model's step-by-step reasoning and solutions.
- Exploring the model's performance on different mathematical benchmarks and datasets, and comparing it to other state-of-the-art models.
- Integrating the model into educational or research tools to enhance mathematical learning and problem-solving capabilities.
- Experimenting with different input parameters, such as top_k, top_p, and temperature, to observe their impact on the model's outputs.
- Investigating the model's ability to generate proofs, derivations, and other mathematical artifacts beyond just problem-solving.
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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