Lb Reranker 0.5B V1.0 GGUF

🚀 Llamacpp imatrix Quantizations of lb-reranker-0.5B-v1.0

This project provides Llama.cpp imatrix quantizations of the lb-reranker-0.5B-v1.0 model. It offers various quantization types to balance between model quality and resource usage, supporting multiple languages and providing detailed download and usage instructions.

📦 Installation

Install huggingface-cli

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Download a specific file

Then, you can target the specific file you want:

huggingface-cli download bartowski/lb-reranker-0.5B-v1.0-GGUF --include "lb-reranker-0.5B-v1.0-Q4_K_M.gguf" --local-dir ./

Download split files

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download bartowski/lb-reranker-0.5B-v1.0-GGUF --include "lb-reranker-0.5B-v1.0-Q8_0/*" --local-dir ./

You can either specify a new local-dir (lb-reranker-0.5B-v1.0-Q8_0) or download them all in place (./)

💻 Usage Examples

Prompt format

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

Run in LM Studio

You can run the quantized models in LM Studio.

📚 Documentation

Model Information

Download Options

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

The project uses llama.cpp release b4456 for quantization. All quants are made using the imatrix option with a 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.

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