🚀 Qwen2.5-VL-3B-Instruct GGUF Models

These files are built using an imatrix file and the latest llama.cpp build. To use the vision feature with the model, you must use a fork of llama.cpp.

🚀 Quick Start

✨ Features

• These GGUF models of Qwen2.5-VL-3B-Instruct are built with specific techniques, enabling vision functionality with a llama.cpp fork.
• The latest quantization method introduces precision - adaptive quantization, proven effective on Llama - 3 - 8B, with layer - specific strategies for accuracy and memory efficiency.
• Multiple model formats are available to suit different hardware capabilities and memory constraints.

📦 Installation

How to Use Qwen 2.5 VL Instruct with llama.cpp

To use the experimental support for Qwen 2.5 VL in llama.cpp, follow these steps. Note that this uses a fork of llama.cpp, and currently, the main branch does not support vision for this model.

1. Clone the latest llama.cpp Fork:

   git clone https://github.com/HimariO/llama.cpp.qwen2vl.git
   cd llama.cpp.qwen2vl
   git checkout qwen25 - vl - 20250404 
   

2. Build the Llama.cpp: Build llama.cpp as usual: https://github.com/ggml-org/llama.cpp#building-the-project. Once llama.cpp is built, copy the ./llama.cpp.qwen2vl/build/bin/llama-qwen2-vl-cli to a chosen folder.
3. Download the Qwen 2.5 VL gguf file: https://huggingface.co/Mungert/Qwen2.5-VL-3B-Instruct-GGUF/tree/main Choose a gguf file without the mmproj in the name. Example gguf file: https://huggingface.co/Mungert/Mungert/Qwen2.5-VL-3B-Instruct-GGUF/resolve/main/Qwen2.5-VL-3B-Instruct-q8_0.gguf Copy this file to your chosen folder.
4. Download the Qwen 2.5 VL mmproj file https://huggingface.co/Mungert/Qwen2.5-VL-3B-Instruct-GGUF/tree/main Choose a file with mmproj in the name. Example mmproj file: https://huggingface.co/Mungert/Qwen2.5-VL-3B-Instruct-GGUF/resolve/main/Qwen2.5-VL-3B-Instruct-mmproj-f16.gguf Copy this file to your chosen folder.
5. Copy images to the same folder as the gguf files or alter paths appropriately. In the example below, the gguf files, images, and llama-qwen2vl-cli are in the same folder. Example image: image Copy this file to your chosen folder.

💻 Usage Examples

Basic Usage

llama-qwen2vl-cli -m Qwen2.5-VL-3B-Instruct-q8_0.gguf --mmproj Qwen2.5-VL-3B-Instruct-mmproj-f16.gguf  -p "Describe this image." --image ./car-1.jpg

Advanced Usage

Quick test with the highest of the DynamicGate quants IQ2_M on Qwen 2.5 VL 3B:

llama.cpp.qwen2vl/build/bin/llama-qwen2vl-cli -m Qwen2.5-VL-3B-Instruct-iq2_m.gguf --mmproj qwen.qwen2.5-vl-3b-instruct-vision.f16.gguf  -p "Describe this image in a lot of detail." --image ./car-1.jpg

🔧 Technical Details

Ultra - Low - Bit Quantization with IQ - DynamicGate (1 - 2 bit)

Our latest quantization method introduces precision - adaptive quantization for ultra - low - bit models (1 - 2 bit), with benchmark - proven improvements on Llama - 3 - 8B. This approach uses layer - specific strategies to preserve accuracy while maintaining extreme memory efficiency.

Benchmark Context

All tests were conducted on Llama - 3 - 8B - Instruct using:

• Standard perplexity evaluation pipeline
• 2048 - token context window
• The same prompt set across all quantizations

Method

• Dynamic Precision Allocation:
  • First/Last 25% of layers → IQ4_XS (selected layers)
  • Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)
• Critical Component Protection:
  • Embeddings/output layers use Q5_K
  • Reduces error propagation by 38% vs standard 1 - 2bit

Quantization Performance Comparison (Llama - 3 - 8B)

Key:

• PPL = Perplexity (lower is better)
• Δ PPL = Percentage change from standard to DynamicGate
• Speed = Inference time (CPU avx2, 2048 token context)
• Size differences reflect mixed quantization overhead

Key Improvements:

• 🔥 IQ1_M shows a massive 43.9% perplexity reduction (27.46 → 15.41)
• 🚀 IQ2_S cuts perplexity by 36.9% while adding only 0.2GB
• ⚡ IQ1_S maintains 39.7% better accuracy despite 1 - bit quantization

Tradeoffs:

• All variants have modest size increases (0.1 - 0.3GB)
• Inference speeds remain comparable (<5% difference)

When to Use These Models

📌 Fitting models into GPU VRAM ✔ Memory - constrained deployments ✔ Cpu and Edge Devices where 1 - 2bit errors can be tolerated ✔ Research into ultra - low - bit quantization

📚 Documentation

Choosing the Right Model Format

Selecting the correct model format depends on your hardware capabilities and memory constraints.

Included Files & Details

📄 License

The license for this project is qwen - research.

🚀 If you find these models useful

Please click like ❤. Also, I’d really appreciate it if you could test my Network Monitor Assistant at 👉 Network Monitor Assitant. 💬 Click the chat icon (bottom right of the main and dashboard pages). Choose an LLM; toggle between the LLM Types TurboLLM -> FreeLLM -> TestLLM.

What I'm Testing

I'm experimenting with function calling against my network monitoring service using small open - source models. I'm exploring the question "How small can it go and still function". 🟡 TestLLM – Runs the current testing model using llama.cpp on 6 threads of a Cpu VM (Should take about 15s to load. Inference speed is quite slow and it only processes one user prompt at a time—still working on scaling!). If you're curious, I'd be happy to share how it works!

The other Available AI Assistants

🟢 TurboLLM – Uses gpt - 4o - mini. Fast! Note: tokens are limited since OpenAI models are pricey, but you can Login or Download the Free Network Monitor agent to get more tokens. Alternatively, use the TestLLM. 🔵 HugLLM – Runs open - source Hugging Face models. Fast, runs small models (≈8B) hence lower quality. Get 2x more tokens (subject to Hugging Face API availability)

Introduction

In the past five months since Qwen2 - VL’s release, numerous developers have built new models on the Qwen2 - VL vision - language models, providing us with valuable feedback. During this period, we focused on building more useful vision - language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5 - VL.

Key Enhancements

• Understand things visually: Qwen2.5 - VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects but is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
• Being agentic: Qwen2.5 - VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use.
• Understanding long videos and capturing events: Qwen2.5 - VL can comprehend videos of over 1 hour, and this time it has a new ability of capturing events by pinpointing the relevant video segments.
• Capable of visual localization in different formats: Qwen2.5 - VL can accurately localize objects in an image by generating bounding boxes or points and can provide stable JSON outputs for coordinates and attributes.
• Generating structured outputs: for data like scans of invoices, forms, tables, etc. Qwen2.5 - VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc.

Model Architecture Updates

• Dynamic Resolution and Frame Rate Training for Video Understanding: We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed and ultimately acquire the ability to pinpoint specific moments.
• Streamlined and Efficient Vision Encoder We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM.

We have three models with 3, 7, and 72 billion parameters. This repo contains the instruction - tuned 3B Qwen2.5 - VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5 - vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5 - VL).

Evaluation

Image benchmark

Video benchmark

Agent benchmark

Requirements

The code of Qwen2.5 - VL has been included in the latest Hugging face transformers. We advise you to build from source with the command:

pip install git+https://github.com/huggingface/transformers accelerate

Otherwise, you might encounter the following error:

KeyError: 'qwen2_5_vl'

Quickstart

Below, we provide simple examples to show how to use Qwen2.5 - VL with 🤖 ModelScope and 🤗 Transformers. The code of Qwen2.5 - VL has been included in the latest Hugging face transformers. We advise you to build from source with the command:

pip install git+https://github.com/huggingface/transformers accelerate

Otherwise, you might encounter the following error:

KeyError: 'qwen2_5_vl'

We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:

# It's highly recommended to use `[decord]` feature for faster video loading.
pip install qwen - vl - utils[decord]==0.0.8

If you are not using Linux, you might not be able to install decord from PyPI. In that case, you can use pip install qwen - vl - utils, which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme - ov - file#install - from - source) to use decord when loading videos.