Arliai QwQ 32B ArliAI RpR V4 GGUF

🚀 Llamacpp imatrix Quantizations of QwQ-32B-ArliAI-RpR-v4 by ArliAI

This project provides llama.cpp imatrix quantizations of the QwQ-32B-ArliAI-RpR-v4 model. It enables efficient deployment and usage of the model in various environments.

🚀 Quick Start

• Run in LM Studio: You can run the quantized models in LM Studio.
• Run with llama.cpp: Use llama.cpp or any other llama.cpp - based project to run the models directly.

✨ Features

• Quantization: Using llama.cpp release b5432 for quantization.
• Diverse Quant Types: Multiple quant types are available to meet different requirements in terms of quality and file size.
• 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 specific files from the following table:

Downloading using huggingface - cli

pip install -U "huggingface_hub[cli]"

huggingface-cli download bartowski/ArliAI_QwQ-32B-ArliAI-RpR-v4-GGUF --include "ArliAI_QwQ-32B-ArliAI-RpR-v4-Q4_K_M.gguf" --local-dir ./

huggingface-cli download bartowski/ArliAI_QwQ-32B-ArliAI-RpR-v4-GGUF --include "ArliAI_QwQ-32B-ArliAI-RpR-v4-Q8_0/*" --local-dir ./

💻 Usage Examples

Prompt format

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

📚 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, 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 increase.

📄 License

This project is licensed under the apache - 2.0 license.