Update README.md

README.md

@@ -1,3 +1,72 @@
----
-license: llama3.1
----
+---
+license: llama3.1
+base_model:
+- meta-llama/Llama-3.1-70B
+datasets:
+- nvidia/OpenMathInstruct-2
+language:
+- en
+tags:
+- nvidia
+- math
+---
+
+# OpenMath2-Llama3.1-70B
+
+OpenMath2-Llama3.1-70B is obtained by finetuning [Llama3.1-70B-Base](https://huggingface.co/meta-llama/Llama-3.1-70B) with [OpenMathInstruct-2](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2).
+
+The model outperforms [Llama3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) on [MATH](https://github.com/hendrycks/math) by 3.9%. 
+
+
+
+| Model | GSM8K | MATH | AMC 2023 | AIME 2024 | Omni-MATH |
+|:---|:---:|:---:|:---:|:---:|:---:|
+| Llama3.1-8B-Instruct | 84.5 | 51.9 | 9/40 | 2/30 | 12.7 |
+| 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 |
+| + majority@256 | 94.1 | 76.1 | 23/40 | 3/30 | 24.6 |
+| Llama3.1-70B-Instruct | 95.8 | 67.9 | 19/40 | 6/30 | 19.0 |
+| **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 |
+| + majority@256 | 96.0 | 79.6 | 24/40 | 6/30 | 27.6 |
+
+The pipeline we used to produce the data and models is fully open-sourced!
+
+- [Code](https://github.com/Kipok/NeMo-Skills)
+- [Models](https://huggingface.co/collections/nvidia/openmath-2-66fb142317d86400783d2c7b)
+- [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2)
+
+
+# How to use the models?
+
+Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands!
+
+# Reproducing our results
+
+We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results.
+
+# Improving other models
+
+To improve other models or to learn more about our code, read through the docs below.
+
+- [NeMo-Skills Pipeline](https://github.com/Kipok/NeMo-Skills)
+    - [Generating synthetic data](https://github.com/Kipok/NeMo-Skills/blob/main/docs/synthetic-data-generation.md)
+    - [Finetuning models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/finetuning.md)
+    - [Evaluating models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/evaluation.md)
+
+In our pipeline we use [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/),
+an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere.
+It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models,
+offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
+
+
+## Citation
+
+If you find our work useful, please consider citing us!
+
+```bibtex
+@article{toshniwal2024openmath2,
+  title   = {OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data},
+  author  = {Shubham Toshniwal and Wei Du and Ivan Moshkov and  Branislav Kisacanin and Alexan Ayrapetyan and Igor Gitman},
+  year    = {2024},
+  journal = {arXiv preprint arXiv:2410.01560}
+}
+```