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README.md
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@@ -48,11 +48,31 @@ The models are evaluated using open-ended and multiple-choice math problems from
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| MAmmoTH | 70B | Llama-2 | 76.7 | 44.2 | 61.4 | 64.3 | 61.7 | 81.7 | 55.3 | 45.3 | 58.6 | 52.3 | 58.6 |
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## Usage
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You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
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Check our Github repo for more advanced use: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH)
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## Intended Uses
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These models are trained for research purposes. They are designed to solve general math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed. The models can generate both a chain of thought (CoT) rationale and a program of thought (PoT) rationale, providing a comprehensive solution to a given math problem.
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| MAmmoTH | 70B | Llama-2 | 76.7 | 44.2 | 61.4 | 64.3 | 61.7 | 81.7 | 55.3 | 45.3 | 58.6 | 52.3 | 58.6 |
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## Usage
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You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution.
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Check our Github repo for more advanced use: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH)
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## Prompt Format
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If you want to do CoT:
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{instruction}
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### Response:
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```
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If you want to do PoT:
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{instruction} Let's write a program.
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### Response:
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```
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## Intended Uses
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These models are trained for research purposes. They are designed to solve general math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed. The models can generate both a chain of thought (CoT) rationale and a program of thought (PoT) rationale, providing a comprehensive solution to a given math problem.
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