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

Libraries

How to use prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Qwen2.5-VL-Abliterated-Caption-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 prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen2.5-VL-Abliterated-Caption-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 prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen2.5-VL-Abliterated-Caption-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 prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Qwen2.5-VL-Abliterated-Caption-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 prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen2.5-VL-Abliterated-Caption-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": "prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Qwen2.5-VL-Abliterated-Caption-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 "prithivMLmods/Qwen2.5-VL-Abliterated-Caption-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": "prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "prithivMLmods/Qwen2.5-VL-Abliterated-Caption-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": "prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Qwen2.5-VL-Abliterated-Caption-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 prithivMLmods/Qwen2.5-VL-Abliterated-Caption-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 prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-VL-Abliterated-Caption-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Qwen2.5-VL-Abliterated-Caption-it-GGUF (3B & 7B)

The Qwen2.5-VL-3B/7B-Abliterated-Caption-it models are fine-tuned variants of Qwen2.5-VL-Instruct architectures, specifically designed for Abliterated (Uncensored) Image Captioning, enabling high-fidelity, richly descriptive captions across diverse and nuanced visual inputs, including sensitive or complex imagery. Building upon the strengths of multimodal Qwen2.5-VL backbones, these models offer robust descriptive power across aspect ratios, artistic and technical visuals, stylized or low-context scenes, and are adaptable to multilingual output via prompt control. Trained on curated datasets combining prithivMLmods/blip3o-caption-mini-arrow, Caption3o-Opt-v2, and private data, they excel at generating unconstrained captions for creative storytelling, dataset enrichment, content moderation research, and generative safety evaluations. While the 3B variant is efficient and lightweight for faster inference, the 7B variant offers enhanced detail, consistency, and reasoning for high-quality outputs. Users should note these models may generate explicit or sensitive captions and are not suited for production with strict content moderation requirements.

Qwen2.5-VL Abliterated Caption (GGUF)

Model	Link
Qwen2.5-VL-3B-Abliterated-Caption-it-GGUF	Hugging Face Repo
Qwen2.5-VL-7B-Abliterated-Caption-it-GGUF	Hugging Face Repo

Model Files

Qwen2.5-VL-3B-Abliterated-Caption-it

File Name	Quant Type	File Size
Qwen2.5-VL-3B-Abliterated-Caption-it.IQ4_XS.gguf	IQ4_XS	1.75 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.Q2_K.gguf	Q2_K	1.27 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.Q3_K_L.gguf	Q3_K_L	1.71 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.Q3_K_M.gguf	Q3_K_M	1.59 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.Q3_K_S.gguf	Q3_K_S	1.45 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.Q4_K_M.gguf	Q4_K_M	1.93 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.Q4_K_S.gguf	Q4_K_S	1.83 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.Q5_K_M.gguf	Q5_K_M	2.22 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.Q5_K_S.gguf	Q5_K_S	2.17 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.Q6_K.gguf	Q6_K	2.54 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.Q8_0.gguf	Q8_0	3.29 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.f16.gguf	F16	6.18 GB
Qwen2.5-VL-3B-Abliterated-Caption-it.mmproj-Q8_0.gguf	MMProj Q8_0	845 MB
Qwen2.5-VL-3B-Abliterated-Caption-it.mmproj-f16.gguf	MMProj F16	1.34 GB

Qwen2.5-VL-7B-Abliterated-Caption-it

File Name	Quant Type	File Size
Qwen2.5-VL-7B-Abliterated-Caption-it.IQ4_XS.gguf	IQ4_XS	4.25 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.Q2_K.gguf	Q2_K	3.02 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.Q3_K_L.gguf	Q3_K_L	4.09 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.Q3_K_M.gguf	Q3_K_M	3.81 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.Q3_K_S.gguf	Q3_K_S	3.49 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.Q4_K_M.gguf	Q4_K_M	4.68 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.Q4_K_S.gguf	Q4_K_S	4.46 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.Q5_K_M.gguf	Q5_K_M	5.44 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.Q5_K_S.gguf	Q5_K_S	5.32 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.Q6_K.gguf	Q6_K	6.25 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.Q8_0.gguf	Q8_0	8.1 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.f16.gguf	F16	15.2 GB
Qwen2.5-VL-7B-Abliterated-Caption-it.mmproj-Q8_0.gguf	MMProj Q8_0	853 MB
Qwen2.5-VL-7B-Abliterated-Caption-it.mmproj-f16.gguf	MMProj F16	1.35 GB

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 1,463

GGUF

Model size

3B params

Architecture

qwen2vl

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF

Base model

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

Finetuned

prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it

Quantized

(5)

this model

Collections including prithivMLmods/Qwen2.5-VL-Abliterated-Caption-GGUF