Qwen2-VL-7B-Instruct-GGUF Open-source Multimodal Model - Supports Joint Understanding and Generation of Images and Texts

Qwen2 VL 7B Instruct GGUF

Developed by tensorblock

Qwen2-VL-7B-Instruct is a multimodal vision-language model that supports the joint understanding and generation of images and text.

Text-to-Image

Transformers

EnglishOpen Source License:Apache-2.0 #Multimodal dialogue #Visual language understanding #Lightweight deployment

Downloads 124

Release Time : 12/15/2024

Model Overview

This model is a vision-language model with a parameter scale of 7B, capable of handling joint tasks of images and text, and suitable for multimodal understanding and generation scenarios.

Model Features

Multimodal support

Capable of simultaneously processing image and text inputs to achieve cross-modal understanding and generation.

GGUF quantization format

Provides GGUF format model files with multiple quantization levels, facilitating deployment on different hardware.

Instruction following

After instruction fine-tuning, it can better understand and execute complex user instructions.

Model Capabilities

Image understanding

Text generation

Multimodal reasoning

Instruction following

Use Cases

Content generation

Image description generation

Generate detailed textual descriptions based on the input images.

Visual question answering

Answer natural language questions about the image content.

Intelligent assistant

Multimodal dialogue

Conduct natural conversations by combining images and text.

🚀 Qwen/Qwen2-VL-7B-Instruct - GGUF

This repository offers GGUF format model files for Qwen/Qwen2-VL-7B-Instruct, enabling efficient multimodal processing.

🚀 Quick Start

This repo contains GGUF format model files for Qwen/Qwen2-VL-7B-Instruct. The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4329.

✨ 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

📚 Documentation

Prompt template

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

Model file specification

Property	Details
Qwen2-VL-7B-Instruct-Q2_K.gguf	Q2_K, 3.016 GB, smallest, significant quality loss - not recommended for most purposes
Qwen2-VL-7B-Instruct-Q3_K_S.gguf	Q3_K_S, 3.492 GB, very small, high quality loss
Qwen2-VL-7B-Instruct-Q3_K_M.gguf	Q3_K_M, 3.808 GB, very small, high quality loss
Qwen2-VL-7B-Instruct-Q3_K_L.gguf	Q3_K_L, 4.088 GB, small, substantial quality loss
Qwen2-VL-7B-Instruct-Q4_0.gguf	Q4_0, 4.431 GB, legacy; small, very high quality loss - prefer using Q3_K_M
Qwen2-VL-7B-Instruct-Q4_K_S.gguf	Q4_K_S, 4.458 GB, small, greater quality loss
Qwen2-VL-7B-Instruct-Q4_K_M.gguf	Q4_K_M, 4.683 GB, medium, balanced quality - recommended
Qwen2-VL-7B-Instruct-Q5_0.gguf	Q5_0, 5.315 GB, legacy; medium, balanced quality - prefer using Q4_K_M
Qwen2-VL-7B-Instruct-Q5_K_S.gguf	Q5_K_S, 5.315 GB, large, low quality loss - recommended
Qwen2-VL-7B-Instruct-Q5_K_M.gguf	Q5_K_M, 5.445 GB, large, very low quality loss - recommended
Qwen2-VL-7B-Instruct-Q6_K.gguf	Q6_K, 6.254 GB, very large, extremely low quality loss
Qwen2-VL-7B-Instruct-Q8_0.gguf	Q8_0, 8.099 GB, very large, extremely low quality loss - not recommended

📦 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/Qwen2-VL-7B-Instruct-GGUF --include "Qwen2-VL-7B-Instruct-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/Qwen2-VL-7B-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

📄 License

This project is licensed under the Apache-2.0 license.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご