Servicenow AI Apriel Nemotron 15b Thinker GGUF

🚀 Llamacpp imatrix Quantizations of Apriel-Nemotron-15b-Thinker by ServiceNow-AI

This project offers quantized versions of the Apriel-Nemotron-15b-Thinker model using llama.cpp. It provides various quantization types to balance between model quality and resource usage, and offers clear guidance on running and downloading the models.

🚀 Quick Start

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

✨ Features

• Multiple Quantization Types: Offers a wide range of quantization types, such as bf16, Q8_0, Q6_K_L, etc., to meet different needs for quality and resource usage.
• Online Repacking: Some quantization types support online repacking for better performance on ARM and AVX machines.

📦 Installation

Downloading using huggingface-cli

pip install -U "huggingface_hub[cli]"

huggingface-cli download bartowski/ServiceNow-AI_Apriel-Nemotron-15b-Thinker-GGUF --include "ServiceNow-AI_Apriel-Nemotron-15b-Thinker-Q4_K_M.gguf" --local-dir ./

huggingface-cli download bartowski/ServiceNow-AI_Apriel-Nemotron-15b-Thinker-GGUF --include "ServiceNow-AI_Apriel-Nemotron-15b-Thinker-Q8_0/*" --local-dir ./

💻 Usage Examples

Prompt format

<|system|>
You are a thoughtful and systematic AI assistant built by ServiceNow Language Models (SLAM) lab. Before providing an answer, analyze the problem carefully and present your reasoning step by step. After explaining your thought process, provide the final solution in the following format: [BEGIN FINAL RESPONSE] ... [END FINAL RESPONSE].

{system_prompt}
<|end|>
<|user|>
{prompt}
<|end|>
<|assistant|>
Here are my reasoning steps:

📚 Documentation

Download a file (not the whole branch)

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.

🔧 Technical Details

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 MIT license.