Virtuoso Lite GGUF

🚀 Llamacpp imatrix Quantizations of Virtuoso-Lite

This project provides Llama.cpp imatrix quantizations of the Virtuoso-Lite model. It uses specific Llama.cpp releases for quantization and offers various quantized models with different characteristics.

🚀 Quick Start

Prerequisites

• Install huggingface-cli if not already installed:

pip install -U "huggingface_hub[cli]"

Running the Model

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

✨ Features

• Multiple Quantization Types: Offers a wide range of quantized models, such as f32, Q8_0, Q6_K_L, etc., to meet different performance and quality requirements.
• Online Repacking: Some models support online repacking of weights for better performance on ARM and AVX machines.
• Prompt Format: Defines a clear prompt format for interacting with the model.

📦 Installation

Downloading a Single File

huggingface-cli download bartowski/Virtuoso-Lite-GGUF --include "Virtuoso-Lite-Q4_K_M.gguf" --local-dir ./

Downloading Split Files

If the model is bigger than 50GB and split into multiple files, run:

huggingface-cli download bartowski/Virtuoso-Lite-GGUF --include "Virtuoso-Lite-Q8_0/*" --local-dir ./

💻 Usage Examples

Prompt Format

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

📚 Documentation

Model Information

Downloadable Files

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.

Which File Should I Choose?

🔧 Technical Details

Quantization Process

The project uses llama.cpp release b4585 for quantization. All quants are made using the imatrix option with a dataset from here.

Online Repacking

The concept of "online repacking" for weights is introduced. For details, refer to this PR. If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

📄 License

The project is under the 'other' 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