Qwen2-VL-2B-GGUF Open-source Vision-Language Model - Free Deployment Suitable for Various Scenarios

Qwen2 VL 2B GGUF

Developed by tensorblock

Qwen2-VL-2B is a vision-language model that provides a quantized version in GGUF format, suitable for various scenarios.

Text-to-Image

Transformers

EnglishOpen Source License:Apache-2.0 #Multimodal Vision-Language #Lightweight Model #GGUF Quantization

Downloads 314

Release Time : 12/15/2024

Model Overview

Qwen2-VL-2B is a vision-language model that supports multimodal processing of images and text, suitable for generating, analyzing, and understanding multimodal content.

Model Features

GGUF Format Support

Provides quantized model files in GGUF format for efficient operation on various hardware.

Multimodal Processing

Supports multimodal processing of images and text, capable of understanding and generating multimodal content.

Rich Quantization Options

Provides various quantization options (such as Q2_K to Q8_0) to meet the needs of different scenarios.

Model Capabilities

Image Understanding

Text Generation

Multimodal Content Analysis

Use Cases

Content Generation

Image Description Generation

Generate detailed text descriptions based on the input image.

Multimodal Analysis

Image-Text Association Analysis

Analyze the association between images and text and generate relevant descriptions or labels.

🚀 Qwen/Qwen2-VL-2B - GGUF

This repository offers GGUF format model files for Qwen/Qwen2-VL-2B. These files are quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4329.

🚀 Quick Start

This section provides a quick guide on how to get started with the Qwen/Qwen2-VL-2B - GGUF model.

✨ Features

Multimodal Support: This model supports image - text - to - text tasks, making it suitable for a wide range of multimodal applications.
GGUF Format: The model files are in GGUF format, which is compatible with llama.cpp.

📦 Installation

Command line

First, install the Huggingface Client:

pip install -U "huggingface_hub[cli]"

Then, download the individual model file to a local directory:

huggingface-cli download tensorblock/Qwen2-VL-2B-GGUF --include "Qwen2-VL-2B-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you want to download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/Qwen2-VL-2B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

📚 Documentation

Our projects

Project	Details
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 👈

Prompt template

Model file specification

Property	Details
Filename	Qwen2-VL-2B-Q2_K.gguf, Qwen2-VL-2B-Q3_K_S.gguf, etc.
Quant type	Q2_K, Q3_K_S, Q3_K_M, etc.
File Size	Ranging from 0.676 GB to 1.647 GB
Description	Varying levels of quality loss and file size trade - offs

Filename	Quant type	File Size	Description
Qwen2-VL-2B-Q2_K.gguf	Q2_K	0.676 GB	smallest, significant quality loss - not recommended for most purposes
Qwen2-VL-2B-Q3_K_S.gguf	Q3_K_S	0.761 GB	very small, high quality loss
Qwen2-VL-2B-Q3_K_M.gguf	Q3_K_M	0.824 GB	very small, high quality loss
Qwen2-VL-2B-Q3_K_L.gguf	Q3_K_L	0.880 GB	small, substantial quality loss
Qwen2-VL-2B-Q4_0.gguf	Q4_0	0.935 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Qwen2-VL-2B-Q4_K_S.gguf	Q4_K_S	0.940 GB	small, greater quality loss
Qwen2-VL-2B-Q4_K_M.gguf	Q4_K_M	0.986 GB	medium, balanced quality - recommended
Qwen2-VL-2B-Q5_0.gguf	Q5_0	1.099 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Qwen2-VL-2B-Q5_K_S.gguf	Q5_K_S	1.099 GB	large, low quality loss - recommended
Qwen2-VL-2B-Q5_K_M.gguf	Q5_K_M	1.125 GB	large, very low quality loss - recommended
Qwen2-VL-2B-Q6_K.gguf	Q6_K	1.273 GB	very large, extremely low quality loss
Qwen2-VL-2B-Q8_0.gguf	Q8_0	1.647 GB	very large, extremely low quality loss - not recommended

📄 License

This project is licensed under the Apache - 2.0 license.

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