llemma_7b
Maintainer: EleutherAI
85
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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 llemma_7b is a language model for mathematics developed by EleutherAI. It was initialized with Code Llama 7B weights and trained on the Proof-Pile-2 dataset for 200 billion tokens. This model also comes in a 34 billion parameter version called Llemma 34B.
Model inputs and outputs
Inputs
- The
llemma_7bmodel takes in text as input.
Outputs
- The model generates text as output, focused on mathematical reasoning and using computational tools for mathematics.
Capabilities
The llemma_7b model is particularly strong at chain-of-thought mathematical reasoning and using computational tools like Python and formal theorem provers. On benchmarks evaluating these capabilities, it outperforms models like Llama-2, Code Llama, and Minerva when controlling for model size.
What can I use it for?
The llemma_7b model could be useful for a variety of mathematics-focused applications, such as:
- Generating step-by-step solutions to mathematical problems
- Assisting with symbolic mathematics and theorem proving
- Providing explanations and examples for mathematical concepts
- Generating code to solve mathematical problems in languages like Python
Things to try
One interesting aspect of the llemma_7b model is its ability to leverage computational tools for mathematics. You could experiment with prompting the model to generate Python code to solve math problems or interact with formal theorem provers. Additionally, the model's strong performance on chain-of-thought reasoning makes it well-suited for open-ended mathematical problem-solving tasks.
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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