Wizardlm 2 7B Abliterated GGUF

🚀 Llamacpp imatrix Quantizations of WizardLM-2-7B-abliterated

This project provides quantized versions of the WizardLM-2-7B-abliterated model using llama.cpp, offering various quantization types to meet different performance and quality requirements.

🚀 Quick Start

Prerequisites

First, make sure you have huggingface-cli installed:

pip install -U "huggingface_hub[cli]"

Download a Specific File

You can target the specific file you want:

huggingface-cli download bartowski/WizardLM-2-7B-abliterated-GGUF --include "WizardLM-2-7B-abliterated-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/WizardLM-2-7B-abliterated-GGUF --include "WizardLM-2-7B-abliterated-Q8_0.gguf/*" --local-dir WizardLM-2-7B-abliterated-Q8_0

You can either specify a new local-dir (e.g., WizardLM-2-7B-abliterated-Q8_0) or download them all in place (.)

✨ Features

• Quantization: Using llama.cpp release b2965 for quantization.
• Multiple Quantization Types: All quants are made using the imatrix option with a dataset from here.

📚 Documentation

Original Model

The original model can be found at: https://huggingface.co/fearlessdots/WizardLM-2-7B-abliterated

Prompt Format

{system_prompt} USER: {prompt} ASSISTANT: </s>

Download Options

You can download a file (not the whole branch) from the following table:

Choosing the Right File

A great write - up with charts showing various performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1 - 2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1 - 2GB smaller than that total.

Next, you'll need to decide if you want to use an 'I - quant' or a 'K - quant'.

If you don't want to think too much, grab one of the K - quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I - quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I - quants can also be used on CPU and Apple Metal, but will be slower than their K - quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

The I - quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double - check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

📄 License

This project is licensed under the Apache 2.0 license.