Nvidia OpenCodeReasoning Nemotron 14B GGUF

🚀 Llamacpp imatrix Quantizations of OpenCodeReasoning-Nemotron-14B by nvidia

This project provides quantized versions of the OpenCodeReasoning-Nemotron-14B model by NVIDIA, enabling efficient deployment and usage.

🚀 Quick Start

This repository contains quantized versions of the OpenCodeReasoning-Nemotron-14B model. You can run these quantized models in LM Studio or directly with llama.cpp, or any other llama.cpp based project.

✨ Features

• Quantization: Using llama.cpp release b5306 for quantization.
• Multiple Quant Types: Offers a variety of quant types to meet different needs, such as BF16, Q8_0, Q6_K_L, etc.
• Online Repacking: Some quant types support online repacking for better performance on ARM and AVX machines.

📦 Installation

Download a file (not the whole branch)

You can download the specific quantized model file from the following table:

Downloading using huggingface-cli

pip install -U "huggingface_hub[cli]"

huggingface-cli download bartowski/nvidia_OpenCodeReasoning-Nemotron-14B-GGUF --include "nvidia_OpenCodeReasoning-Nemotron-14B-Q4_K_M.gguf" --local-dir ./

huggingface-cli download bartowski/nvidia_OpenCodeReasoning-Nemotron-14B-GGUF --include "nvidia_OpenCodeReasoning-Nemotron-14B-Q8_0/*" --local-dir ./

💻 Usage Examples

Prompt format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

📚 Documentation

Embed/output weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

ARM/AVX information

Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.

Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

As of llama.cpp build b4282 you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.

Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.

📄 License

This project is licensed under the Apache-2.0 license.