Qwen Qwen2.5 VL 7B Instruct GGUF

🚀 Llamacpp imatrix Quantizations of Qwen2.5-VL-7B-Instruct by Qwen

This project provides quantized versions of the Qwen2.5-VL-7B-Instruct model using llama.cpp. It offers various quantization types to balance between model quality and resource usage, making it suitable for different hardware configurations.

🚀 Quick Start

• Run in LM Studio: You can run the quantized models in LM Studio.
• Run with llama.cpp: Directly run the models with llama.cpp or any other llama.cpp - based project.

✨ Features

• Multiple Quantization Types: Offers a wide range of quantization types such as bf16, Q8_0, Q6_K_L, etc., allowing users to choose based on their hardware and quality requirements.
• Optimized for Different Architectures: Some quantizations are optimized for ARM and AVX machines, and support online repacking for better performance.

📦 Installation

Install huggingface-cli

First, make sure you have huggingface-cli installed:

pip install -U "huggingface_hub[cli]"

Download a Specific File

huggingface-cli download bartowski/Qwen_Qwen2.5-VL-7B-Instruct-GGUF --include "Qwen_Qwen2.5-VL-7B-Instruct-Q4_K_M.gguf" --local-dir ./

Download Split Files

If the model is bigger than 50GB and split into multiple files, run:

huggingface-cli download bartowski/Qwen_Qwen2.5-VL-7B-Instruct-GGUF --include "Qwen_Qwen2.5-VL-7B-Instruct-Q8_0/*" --local-dir ./

💻 Usage Examples

Prompt Format

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

📚 Documentation

Download a File

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, 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 increase.

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'.

🔧 Technical Details

The quantization is performed using llama.cpp release b5317. All quants are made using the imatrix option with a dataset from here.

📄 License

This project is licensed under the apache - 2.0 license.