Nvidia AceReason Nemotron 1.1 7B GGUF

🚀 Llamacpp imatrix Quantizations of AceReason-Nemotron-1.1-7B by nvidia

This project provides Llama.cpp imatrix quantizations of the AceReason-Nemotron-1.1-7B model by NVIDIA. It offers various quantization types to suit different hardware and performance requirements.

🚀 Quick Start

Running the Model

• You can run the quantized models in LM Studio.
• Run them directly with llama.cpp, or any other llama.cpp based project.

Prompt Format

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

✨ Features

• Multiple Quantization Types: Offers a wide range of quantization types, such as bf16, Q8_0, Q6_K_L, etc., to balance between model quality and file size.
• Online Repacking: Some quantization types support online repacking of weights, which can improve performance on ARM and AVX machines.
• Flexible Download Options: Allows users to download specific files or split files using the huggingface-cli.

📦 Installation

Prerequisites

Make sure you have huggingface-cli installed:

pip install -U "huggingface_hub[cli]"

Downloading a Specific File

To download a specific file, use the following command:

huggingface-cli download bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF --include "nvidia_AceReason-Nemotron-1.1-7B-Q4_K_M.gguf" --local-dir ./

Downloading Split Files

If the model is split into multiple files, run the following command to download them all to a local folder:

huggingface-cli download bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF --include "nvidia_AceReason-Nemotron-1.1-7B-Q8_0/*" --local-dir ./

💻 Usage Examples

Choosing the Right Quantization File

📚 Documentation

Download a File

You can download a file (not the whole branch) from the following table:

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.

🔧 Technical Details

Quantization Process

Using llama.cpp release b5674 for quantization. All quants are made using the imatrix option with a dataset from here.

Online Repacking

As of llama.cpp build b4282, the Q4_0 format supports online repacking of weights for ARM and AVX machines. If your hardware would benefit from repacking, it will be done automatically on the fly.

📄 License

This project is licensed under the nvidia-open-model-license.

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset. Thank you ZeroWw for the inspiration to experiment with embed/output. Thank you to LM Studio for sponsoring my work.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski