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README.md

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+
+---
+
+license: llama3.1
+base_model:
+- meta-llama/Llama-3.1-8B
+datasets:
+- nvidia/OpenMathInstruct-2
+language:
+- en
+tags:
+- nvidia
+- math
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/OpenMath2-Llama3.1-8B-GGUF
+This is quantized version of [nvidia/OpenMath2-Llama3.1-8B](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B) created using llama.cpp
+
+# Original Model Card
+
+
+# OpenMath2-Llama3.1-8B
+
+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%. 
+
+<!-- <p align="center">
+  <img src="scaling_plot.jpg" width="350"><img src="math_level_comp.jpg" width="350">
+</p> -->
+
+  <style>
+      .image-container {
+          display: flex;
+          justify-content: center;
+          align-items: center;
+          gap: 20px;
+      }
+      .image-container img {
+          width: 350px;
+          height: auto;
+      }
+  </style>
+
+<div class="image-container">
+    <img src="scaling_plot.jpg" title="Performance of Llama-3.1-8B-Instruct as it is trained on increasing proportions of OpenMathInstruct-2">
+    <img src="math_level_comp.jpg" title="Comparison of OpenMath2-Llama3.1-8B vs. Llama-3.1-8B-Instruct across MATH levels">
+</div>
+
+| 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)
+
+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). 
+Please note that these models have not been instruction tuned on general data and thus might not provide good answers outside of math domain. 
+
+We recommend using [instructions in our repo](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) to run inference with these models, but here is
+an example of how to do it through transformers api:
+
+```python
+import transformers
+import torch
+
+model_id = "nvidia/OpenMath2-Llama3.1-8B"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {
+        "role": "user", 
+        "content": "Solve the following math problem. Make sure to put the answer (and only answer) inside \\boxed{}.\n\n" + 
+        "What is the minimum value of $a^2+6a-7$?"},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=4096,
+)
+print(outputs[0]["generated_text"][-1]['content'])
+```
+
+# Reproducing our results
+
+We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results.
+
+## 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}
+}
+```
+
+## Terms of use
+
+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/)
+