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---
license: llama3.1
language:
- en
base_model:
- nvidia/OpenMath2-Llama3.1-8B
pipeline_tag: text-generation
tags:
- math
- nvidia
- llama
---
## GGUF quantized version of OpenMath2-Llama3.1-8B
project original [source](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B) (finetuned model)
Q_2_K (not nice)
Q_3_K_S (acceptable)
Q_3_K_M is acceptable (good for running with CPU)
Q_3_K_L (acceptable)
Q_4_K_S (okay)
Q_4_K_M is recommanded (balance)
Q_5_K_S (good)
Q_5_K_M (good in general)
Q_6_K is good also; if you want a better result; take this one instead of Q_5_K_M
Q_8_0 which is very good; need a reasonable size of RAM otherwise you might expect a long wait
f16 is similar to the original hf model; opt this one or hf also fine; make sure you have a good machine
### how to run it
use any connector for interacting with gguf; i.e., [gguf-connector](https://pypi.org/project/gguf-connector/)
<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="https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B/resolve/main/scaling_plot.jpg" title="Performance of Llama-3.1-8B-Instruct as it is trained on increasing proportions of OpenMathInstruct-2">
<img src="https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B/resolve/main/math_level_comp.jpg" title="Comparison of OpenMath2-Llama3.1-8B vs. Llama-3.1-8B-Instruct across MATH levels">
</div>
the chart and figure above are from finetuned model (nvidia side); those are used for comparing between the finetuned model and the base model; and the base model is from meta |