Qwen2.5 Coder 1.5B Instruct Abliterated GGUF

🚀 Llamacpp imatrix Quantizations of Qwen2.5-Coder-1.5B-Instruct-abliterated

This project provides quantized versions of the Qwen2.5-Coder-1.5B-Instruct-abliterated model using llama.cpp, offering various quantization types for different performance and quality requirements.

🚀 Quick Start

Run in LM Studio

You can run these quantized models in LM Studio.

Download a File

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

✨ Features

• Quantization: Using llama.cpp release b4058 for quantization.
• Multiple Quantization Types: Offers a variety of quantization types to meet different performance and quality requirements.
• ARM Optimization: Some quantizations are optimized for ARM chips, providing substantial speedup.

📦 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/Qwen2.5-Coder-1.5B-Instruct-abliterated-GGUF --include "Qwen2.5-Coder-1.5B-Instruct-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/Qwen2.5-Coder-1.5B-Instruct-abliterated-GGUF --include "Qwen2.5-Coder-1.5B-Instruct-abliterated-Q8_0/*" --local-dir ./

You can either specify a new local-dir (Qwen2.5-Coder-1.5B-Instruct-abliterated-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

📚 Documentation

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.

Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.

Thanks!

Q4_0_X_X

These are NOT for Metal (Apple) offloading, only ARM chips.

If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons on the original pull request

To check which one would work best for your ARM chip, you can check AArch64 SoC features (thanks EloyOn!).

Which file should I choose?

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.

🔧 Technical Details

• Quantization Method: All quants are made using the imatrix option with a dataset from here.
• Original Model: The original model can be found at https://huggingface.co/huihui-ai/Qwen2.5-Coder-1.5B-Instruct-abliterated.

📄 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