Add pipeline tag, clarify license, add library and second citation

README.md

@@ -1,11 +1,13 @@
 ---
-license: llama3.1
 base_model:
 - meta-llama/Llama-3.1-8B
 datasets:
 - nvidia/OpenMathInstruct-2
 language:
 - en
+license: mit
+pipeline_tag: text-generation
+library_name: transformers
 tags:
 - nvidia
 - math
@@ -15,7 +17,7 @@ tags:
 
 [NeMo](https://github.com/NVIDIA/NeMo) checkpoint for [OpenMath2-Llama3.1-8B](https://huggingface.co/nvidia/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).
 
-The model outperforms [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on all the popular math benchmarks we evaluate on, especially on [MATH](https://github.com/hendrycks/math) by 15.9%. 
+The model outperforms [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on all the popular math benchmarks we evaluate on, especially on [MATH](https://github.com/hendrycks/math) by 15.9%.
 
 <!-- <p align="center">
   <img src="scaling_plot.jpg" width="350"><img src="math_level_comp.jpg" width="350">
@@ -58,12 +60,12 @@ See our [paper](https://arxiv.org/abs/2410.01560) to learn more details!
 
 # How to use the models?
 
-Our models are trained with the same "chat format" as Llama3.1-instruct models (same system/user/assistant tokens). 
+Our models are trained with the same "chat format" as Llama3.1-instruct models (same system/user/assistant tokens).
 Please note that these models have not been instruction tuned on general data and thus might not provide good answers outside of math domain.
 
-This is a NeMo checkpoint, so you need to use [NeMo Framework](https://github.com/NVIDIA/NeMo) to run inference or finetune it. 
+This is a NeMo checkpoint, so you need to use [NeMo Framework](https://github.com/NVIDIA/NeMo) to run inference or finetune it.
 We also release a [HuggingFace checkpoint](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B) and provide easy instructions on how to
-[convert between different formats](https://nvidia.github.io/NeMo-Skills/pipelines/checkpoint-conversion/) or 
+[convert between different formats](https://nvidia.github.io/NeMo-Skills/pipelines/checkpoint-conversion/) or
 [run inference](https://nvidia.github.io/NeMo-Skills/basics/inference/) with these models using our codebase.
 
 # Reproducing our results
@@ -75,7 +77,7 @@ We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathinstr
 If you find our work useful, please consider citing us!
 
 ```bibtex
-@article{toshniwal2024openmath2,
+@article{toshniwal2024openmathinstruct2,
   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},
@@ -83,6 +85,13 @@ If you find our work useful, please consider citing us!
 }
 ```
 
-## Terms of use
+```bibtex
+@inproceedings{toshniwal2024openmathinstruct1,
+  title   = {{OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset}},
+  author  = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
+  year    = {2024},
+  booktitle = {Advances in Neural Information Processing Systems},
+}
+```
 
-By accessing this model, you are agreeing to the LLama 3.1 terms and conditions of the [license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE), [acceptable use policy](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/USE_POLICY.md) and [Meta’s privacy policy](https://www.facebook.com/privacy/policy/)
+Disclaimer: This project is strictly for research purposes, and not an official product from NVIDIA.