đ Qwen/QVQ-72B-Preview - GGUF
This repository offers GGUF format model files for Qwen/QVQ-72B-Preview, quantized with the help of TensorBlock, and compatible with llama.cpp.

đ Quick Start
This repo contains GGUF format model files for Qwen/QVQ-72B-Preview. The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4391.
⨠Features
Our projects
Project |
Description |
Image |
Link |
Awesome MCP Servers |
A comprehensive collection of Model Context Protocol (MCP) servers. |
 |
See what we built |
TensorBlock Studio |
A lightweight, open, and extensible multi-LLM interaction studio. |
 |
See what we built |
đ Documentation
Prompt template
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
Model file specification
Filename |
Quant type |
File Size |
Description |
QVQ-72B-Preview-Q2_K.gguf |
Q2_K |
29.812 GB |
smallest, significant quality loss - not recommended for most purposes |
QVQ-72B-Preview-Q3_K_S.gguf |
Q3_K_S |
34.488 GB |
very small, high quality loss |
QVQ-72B-Preview-Q3_K_M.gguf |
Q3_K_M |
37.699 GB |
very small, high quality loss |
QVQ-72B-Preview-Q3_K_L.gguf |
Q3_K_L |
39.505 GB |
small, substantial quality loss |
QVQ-72B-Preview-Q4_0.gguf |
Q4_0 |
41.232 GB |
legacy; small, very high quality loss - prefer using Q3_K_M |
QVQ-72B-Preview-Q4_K_S.gguf |
Q4_K_S |
43.889 GB |
small, greater quality loss |
QVQ-72B-Preview-Q4_K_M.gguf |
Q4_K_M |
47.416 GB |
medium, balanced quality - recommended |
QVQ-72B-Preview-Q5_0 |
Q5_0 |
50.164 GB |
legacy; medium, balanced quality - prefer using Q4_K_M |
QVQ-72B-Preview-Q5_K_S |
Q5_K_S |
51.375 GB |
large, low quality loss - recommended |
QVQ-72B-Preview-Q5_K_M |
Q5_K_M |
54.447 GB |
large, very low quality loss - recommended |
QVQ-72B-Preview-Q6_K |
Q6_K |
64.348 GB |
very large, extremely low quality loss |
QVQ-72B-Preview-Q8_0 |
Q8_0 |
77.263 GB |
very large, extremely low quality loss - not recommended |
đĻ Installation
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, download the individual model file to a local directory
huggingface-cli download tensorblock/QVQ-72B-Preview-GGUF --include "QVQ-72B-Preview-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/QVQ-72B-Preview-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
đ License