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--- |
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license: llama3.1 |
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base_model: |
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- meta-llama/Llama-3.1-8B |
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datasets: |
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- nvidia/OpenMathInstruct-2 |
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language: |
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- en |
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tags: |
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- nvidia |
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- math |
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--- |
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# OpenMath2-Llama3.1-8B |
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OpenMath2-Llama3.1-8B is obtained by finetuning [Llama3.1-8B-Base](https://huggingface.co./meta-llama/Llama-3.1-8B) with [OpenMathInstruct-2](https://huggingface.co./datasets/nvidia/OpenMathInstruct-2). |
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The model outperforms [Llama3.1-8B-Instruct](https://huggingface.co./meta-llama/Llama-3.1-8B-Instruct) on [MATH](https://github.com/hendrycks/math) by 3.9%. |
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| Model | GSM8K | MATH | AMC 2023 | AIME 2024 | Omni-MATH | |
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|:---|:---:|:---:|:---:|:---:|:---:| |
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| Llama3.1-8B-Instruct | 84.5 | 51.9 | 9/40 | 2/30 | 12.7 | |
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| **OpenMath2-Llama3.1-8B** ([nemo](https://huggingface.co./nvidia/OpenMath2-Llama3.1-8B-nemo) \| [HF](https://huggingface.co./nvidia/OpenMath2-Llama3.1-8B)) | 91.7 | 67.8 | 16/40 | 3/30 | 22.0 | |
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| + majority@256 | 94.1 | 76.1 | 23/40 | 3/30 | 24.6 | |
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| Llama3.1-70B-Instruct | 95.8 | 67.9 | 19/40 | 6/30 | 19.0 | |
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| OpenMath2-Llama3.1-70B ([nemo](https://huggingface.co./nvidia/OpenMath2-Llama3.1-70B-nemo) \| [HF](https://huggingface.co./nvidia/OpenMath2-Llama3.1-70B)) | 94.9 | 71.9 | 20/40 | 4/30 | 23.1 | |
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| + majority@256 | 96.0 | 79.6 | 24/40 | 6/30 | 27.6 | |
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The pipeline we used to produce the data and models is fully open-sourced! |
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- [Code](https://github.com/Kipok/NeMo-Skills) |
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- [Models](https://huggingface.co./collections/nvidia/openmath-2-66fb142317d86400783d2c7b) |
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- [Dataset](https://huggingface.co./datasets/nvidia/OpenMathInstruct-2) |
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# How to use the models? |
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Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands! |
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# Reproducing our results |
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We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results. |
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# Improving other models |
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To improve other models or to learn more about our code, read through the docs below. |
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- [NeMo-Skills Pipeline](https://github.com/Kipok/NeMo-Skills) |
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- [Generating synthetic data](https://github.com/Kipok/NeMo-Skills/blob/main/docs/synthetic-data-generation.md) |
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- [Finetuning models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/finetuning.md) |
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- [Evaluating models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/evaluation.md) |
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In our pipeline we use [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/), |
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an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere. |
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It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, |
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offering enterprises an easy, cost-effective, and fast way to adopt generative AI. |
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## Citation |
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If you find our work useful, please consider citing us! |
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```bibtex |
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@article{toshniwal2024openmath2, |
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title = {OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data}, |
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author = {Shubham Toshniwal and Wei Du and Ivan Moshkov and Branislav Kisacanin and Alexan Ayrapetyan and Igor Gitman}, |
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year = {2024}, |
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journal = {arXiv preprint arXiv:2410.01560} |
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} |
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``` |
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