Instructions to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF",
	filename="Qwen2.5-VL-7B-Instruct-mmproj.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M

SGLang

How to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF to start chatting

Pi new

How to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-VL-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Quantized Qwen2.5-VL-7B-Instruct

This repository provides Quantized version of the instruction-tuned multimodal version of the Qwen2.5-VL-7B-Instruct model.This includes both Q4_K_M and Q5_K_M version. The model extends language capabilities with powerful visual and video understanding, structured output generation, and agentic behaviours. Optimized for tasks requiring both text and vision inputs, the model sets new benchmarks in document parsing, OCR, chart understanding, and long video reasoning.

Model Overview

Original Model: Qwen2.5-VL-7B
Variants: Instruct-tuned multimodal model
Architecture: Vision-Language Transformer with decoder-only backbone
Base Model: Qwen2.5-7B-Instruct
Modalities: Text, Image, Video
Quantized Version:
- Q4_K_M (4-bit quantization)
- Q5_K_M (5-bit quantization)
Developer: Qwen
License: Apache 2.0 License
Languages: English, Chinese

Quantization Details

Q4_K_M Version

Approx. ~40% size reduction
Lower memory footprint (~9 GB)
Well-suited for deployment on edge devices or low-resource GPUs
Minor performance degradation in highly complex reasoning scenarios

Q5_K_M Version

Approx. ~34% size reduction
Lower memory footprint (~10 GB)
Better performance retention, recommended when quality is a priority

Key Features

Advanced visual perception: recognizes natural images, charts, plots, forms, and multilingual text.
Long video reasoning: understands and localizes events in videos exceeding 1 hour in length.
Agentic abilities: supports UI control, tool-use, and interactive multimodal tasks.
Structured outputs: can generate bounding boxes, keypoints, and JSON-formatted structured responses.
Dynamic resolution handling and efficient temporal encoding for video tasks.

Dataset Highlights

Post-training corpus enlarged from ~1M samples / 1.2B tokens to ~5M samples / ~60B tokens combining reasoning and non-reasoning data.
Emphasis on reasoning traces, schema adherence (valid JSON, format compliance), and reduced refusal.
Supports tool / function-calling outputs and structured output formats

Usage Example

Text-Only Inference:

./llama-cli -hf SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF -p "Explain Transformer architecture"

Multi-Modal Inference:

./llava-cli -m ./models/SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF --mmproj ./models/Qwen2.5-VL-7B-Instruct-mmproj.gguf --image ./examples/chart.png -p "What does this chart represent?"

Recommended Use Cases

Document understanding Extract structured information from forms, invoices, and tables.
Visual question answering Handle reasoning over complex images, charts, and diagrams.
Video reasoning and summarization Identify key moments and provide natural language summaries.
Agent-style interactions Power multimodal AI agents capable of interacting with digital environments.

Acknowledgments

These quantized models are based on the original work by the Qwen development team.

Special thanks to:

The Qwen team for developing and releasing the Qwen2.5-VL-7B-Instruct model.
Georgi Gerganov and the entire llama.cpp open-source community for enabling efficient model quantization and inference via the GGUF format.

Contact

For any inquiries or support, please contact us at support@sandlogic.com or visit our Website.

Downloads last month: 49

GGUF

Model size

15B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

Model tree for SandLogicTechnologies/Qwen2.5-VL-7B-Instruct-GGUF

Base model

Qwen/Qwen2.5-VL-7B-Instruct

Quantized

(138)

this model