README.md

metadata

license: cc-by-nc-nd-4.0
base_model: rizla/rizla-17
tags:
  - dpo
  - merge
  - mergekit
  - TensorBlock
  - GGUF

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rizla/rizla-17 - GGUF

This repo contains GGUF format model files for rizla/rizla-17.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Run them on the TensorBlock client using your local machine ↗

Prompt template

Model file specification

Filename	Quant type	File Size	Description
rizla-17-Q2_K.gguf	Q2_K	5.769 GB	smallest, significant quality loss - not recommended for most purposes
rizla-17-Q3_K_S.gguf	Q3_K_S	6.774 GB	very small, high quality loss
rizla-17-Q3_K_M.gguf	Q3_K_M	7.522 GB	very small, high quality loss
rizla-17-Q3_K_L.gguf	Q3_K_L	8.166 GB	small, substantial quality loss
rizla-17-Q4_0.gguf	Q4_0	8.834 GB	legacy; small, very high quality loss - prefer using Q3_K_M
rizla-17-Q4_K_S.gguf	Q4_K_S	8.895 GB	small, greater quality loss
rizla-17-Q4_K_M.gguf	Q4_K_M	9.430 GB	medium, balanced quality - recommended
rizla-17-Q5_0.gguf	Q5_0	10.772 GB	legacy; medium, balanced quality - prefer using Q4_K_M
rizla-17-Q5_K_S.gguf	Q5_K_S	10.772 GB	large, low quality loss - recommended
rizla-17-Q5_K_M.gguf	Q5_K_M	11.079 GB	large, very low quality loss - recommended
rizla-17-Q6_K.gguf	Q6_K	12.832 GB	very large, extremely low quality loss
rizla-17-Q8_0.gguf	Q8_0	16.619 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/rizla-17-GGUF --include "rizla-17-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/rizla-17-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'