bartowski/SicariusSicariiStuff_Llama-3.3-8B-Instruct-128K_Abliterated-GGUF

quantized_by: bartowski
pipeline_tag: text-generation
base_model_relation: quantized
base_model: SicariusSicariiStuff/Llama-3.3-8B-Instruct-128K_Abliterated
license: llama3.3
language:

• en
  widget:
• text: Llama-3.3-8B-Instruct-128K_Abliterated
  output:
  url: https://huggingface.co/SicariusSicariiStuff/Llama-3.3-8B-Instruct-128K_Abliterated/resolve/main/Images/Llama-3.3-8B-Instruct-128K_Abliterated.png

Llamacpp imatrix Quantizations of Llama-3.3-8B-Instruct-128K_Abliterated by SicariusSicariiStuff

Using llama.cpp release b7610 for quantization.

Original model: https://huggingface.co/SicariusSicariiStuff/Llama-3.3-8B-Instruct-128K_Abliterated

All quants made using imatrix option with dataset from here

Run them in your choice of tools:

• llama.cpp
• LM Studio
• koboldcpp
• Jan AI
• Text Generation Web UI
• LoLLMs

Note: if it's a newly supported model, you may need to wait for an update from the developers.

Prompt format

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 30 Dec 2025

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

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.

Downloading using huggingface-cli

pip install -U "huggingface_hub[cli]"

huggingface-cli download bartowski/SicariusSicariiStuff_Llama-3.3-8B-Instruct-128K_Abliterated-GGUF --include "SicariusSicariiStuff_Llama-3.3-8B-Instruct-128K_Abliterated-Q4_K_M.gguf" --local-dir ./

huggingface-cli download bartowski/SicariusSicariiStuff_Llama-3.3-8B-Instruct-128K_Abliterated-GGUF --include "SicariusSicariiStuff_Llama-3.3-8B-Instruct-128K_Abliterated-Q8_0/*" --local-dir ./

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?

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