Instructions to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_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 Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_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 Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

Use Docker

docker model run hf.co/Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_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": "Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

Ollama
How to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with Ollama:
```
ollama run hf.co/Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with Docker Model Runner:
```
docker model run hf.co/Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M
```

Lemonade

How to use Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF(Vision Language)

This repository hosts Qwen2.5-VL-Abliterated-Caption-GGUF, a quantized Vision-Language (Uncensored) model optimized for image understanding and caption generation with relaxed alignment constraints. The model is designed for local inference, experimentation, and research-oriented multimodal workflows.

It targets users who want direct, descriptive visual reasoning without heavy content moderation layers, packaged in a GGUF format for efficient CPU and edge-device deployment.

Model Summary

Model Identifier: Qwen2.5-VL-Abliterated-Caption-GGUF
Base Model: Qwen2.5-VL (Vision-Language)
Architecture: Transformer-based multimodal model (text + vision)
Original model: prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF
Primary Function: Image captioning and visual-text understanding

###Purpose & Design Goals

This variant prioritizes expressive visual descriptions and caption accuracy while minimizing restrictive alignment behaviors. The “abliterated” aspect indicates reduced policy-driven refusals, making the model more suitable for:

Dataset generation
Visual analysis research
Creative or descriptive captioning tasks
Offline or private multimodal pipelines

Multimodal Interaction Format

The model follows a standard multimodal prompt structure compatible with Qwen-VL style templates. A typical interaction may include system context, a user query, and an image reference:

<|system|>
You are a visual captioning assistant.
<|user|>
Describe the image in detail.
<|vision_input|>
<image>
<|assistant|>

Core Capabilities

Detailed and literal image captioning
Multimodal reasoning over visual scenes
Object, action, and context recognition
Long-form descriptive outputs
Reduced refusal behavior compared to safety-aligned VL models
Optimized for local inference via GGUF

Recommended Use Cases

Image caption generation – datasets, tagging, annotation
Visual analysis – scene breakdowns, object relationships
Creative workflows – storytelling from images
Research & evaluation – alignment and multimodal behavior testing
Offline deployments – no cloud or API dependency

Credits & Acknowledgements

Qwen team for the base Qwen2.5-VL architecture
GGUF tooling and local inference ecosystem contributors
Open-source multimodal research community

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GGUF

Model size

3B params

Architecture

qwen2vl

Hardware compatibility

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Model tree for Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF

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Collection including Andycurrent/Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF

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