Gryphe Pantheon RP 1.8 24b Small 3.1 GGUF

🚀 Llamacpp imatrix Quantizations of Pantheon-RP-1.8-24b-Small-3.1 by Gryphe

This project provides Llama.cpp imatrix quantizations of the Pantheon-RP-1.8-24b-Small-3.1 model. It offers various quantization types to meet different performance and quality requirements, making it easier to run the model on different hardware.

📦 Metadata

🚀 Quick Start

• Quantization: Using llama.cpp release b4896 for quantization.
• Original Model: You can find the original model at here.
• 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 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, which can improve performance on ARM and AVX machines.

📦 Installation

Downloading using huggingface-cli

pip install -U "huggingface_hub[cli]"

huggingface-cli download bartowski/Gryphe_Pantheon-RP-1.8-24b-Small-3.1-GGUF --include "Gryphe_Pantheon-RP-1.8-24b-Small-3.1-Q4_K_M.gguf" --local-dir ./

huggingface-cli download bartowski/Gryphe_Pantheon-RP-1.8-24b-Small-3.1-GGUF --include "Gryphe_Pantheon-RP-1.8-24b-Small-3.1-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

Download a file (not the whole branch) from below:

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

All quants are made using the imatrix option with the dataset from here.

📄 License

This project is licensed under the Apache-2.0 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