deepseek-math-7b-base
Maintainer: deepseek-ai
651
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Model overview
deepseek-math-7b-base is a large language model (LLM) developed by DeepSeek AI, a leading AI research company. The model is part of the DeepSeekMath series, which focuses on pushing the limits of mathematical reasoning in open language models. The base model is initialized with DeepSeek-Coder-v1.5 7B and continues pre-training on math-related tokens from Common Crawl, natural language, and code data for a total of 500B tokens. This model has achieved an impressive score of 51.7% on the competition-level MATH benchmark, approaching the performance of Gemini-Ultra and GPT-4 without relying on external toolkits or voting techniques.
The DeepSeekMath series also includes instructed (deepseek-math-7b-instruct) and reinforcement learning (deepseek-math-7b-rl) variants, which demonstrate even stronger mathematical capabilities. The instructed model is derived from the base model with further mathematical training, while the RL model is trained on top of the instructed model using a novel Group Relative Policy Optimization (GRPO) algorithm.
Model inputs and outputs
Inputs
- text: The input text to be processed by the model, such as a mathematical problem or a natural language prompt.
- top_k: The number of highest probability vocabulary tokens to keep for top-k-filtering during text generation.
- top_p: If set to a float less than 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 during text generation.
- max_new_tokens: The maximum number of new tokens to generate, ignoring the number of tokens in the prompt.
Outputs
The model outputs a sequence of generated text, which can be a step-by-step solution to a mathematical problem, a natural language response to a prompt, or a combination of both.
Capabilities
The deepseek-math-7b-base model demonstrates superior mathematical reasoning capabilities, outperforming existing open-source base models by more than 10% on the competition-level MATH dataset through few-shot chain-of-thought prompting. It also shows strong tool use ability, leveraging its foundations in DeepSeek-Coder-Base-7B-v1.5 to effectively solve and prove mathematical problems by writing programs. Additionally, the model achieves comparable performance to DeepSeek-Coder-Base-7B-v1.5 in natural language reasoning and coding tasks.
What can I use it for?
The deepseek-math-7b-base model, along with its instructed and RL variants, can be used for a wide range of applications that require advanced mathematical reasoning and problem-solving abilities. Some potential use cases include:
- Educational tools: The model can be used to develop interactive math tutoring systems, homework assistants, or exam preparation tools.
- Scientific research: Researchers in fields like physics, engineering, or finance can leverage the model's mathematical capabilities to aid in problem-solving, data analysis, and theorem proving.
- AI-powered productivity tools: The model's ability to generate step-by-step solutions and write programs can be integrated into productivity tools to boost efficiency in various mathematical and technical tasks.
- Conversational AI: The model's natural language understanding and generation capabilities can be used to build advanced chatbots and virtual assistants that can engage in meaningful mathematical discussions.
Things to try
One interesting aspect of the deepseek-math-7b-base model is its ability to tackle mathematical problems using a combination of step-by-step reasoning and tool use. Users can experiment with prompts that require the model to not only solve a problem but also explain its reasoning and, if necessary, write code to aid in the solution. This can help users better understand the model's unique approach to mathematical problem-solving.
Additionally, users can explore the model's performance on a diverse range of mathematical domains, from algebra and calculus to probability and statistics, to gain insights into its strengths and limitations. Comparing the model's outputs with those of human experts or other AI systems can also yield valuable insights.
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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