Instructions to use prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-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/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF to start chatting

How to use prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-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": "prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-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 prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen3-VL-2B-Instruct-abliterated-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-VL-2B-Instruct-abliterated-v1-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3-VL-2B-Instruct-abliterated-v1-GGUF

The Qwen3-VL-2B-Instruct-abliterated-v1 from prithivMLmods is a 2B-parameter vision-language model variant of Alibaba's Qwen3-VL-2B-Instruct, modified through abliteration (v1.0) to remove safety refusals and content filters, enabling uncensored, detailed captioning and multimodal reasoning across sensitive, complex, artistic, technical, or abstract visual content while preserving the base model's strong instruction-following and visual agent capabilities. Optimized for high-fidelity descriptions with variational detail control—from concise summaries to intricate, step-by-step narratives—it handles diverse aspect ratios, resolutions, and layouts robustly, supporting primarily English outputs with multilingual adaptability via prompt engineering for research, red-teaming, and creative applications. Built on Qwen3-VL's advanced image comprehension, this abliterated version bypasses conventional guardrails to deliver factual, descriptive responses without censorship, making it suitable for unrestricted visual analysis in local deployments.

Qwen3-VL-2B-Instruct-abliterated-v1 [GGUF]

File Name	Quant Type	File Size	File Link
Qwen3-VL-2B-Instruct-abliterated-v1.IQ4_XS.gguf	IQ4_XS	1.02 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.Q2_K.gguf	Q2_K	778 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.Q3_K_L.gguf	Q3_K_L	1 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.Q3_K_M.gguf	Q3_K_M	940 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.Q3_K_S.gguf	Q3_K_S	867 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.Q4_K_M.gguf	Q4_K_M	1.11 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.Q4_K_S.gguf	Q4_K_S	1.06 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.Q5_K_M.gguf	Q5_K_M	1.26 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.Q5_K_S.gguf	Q5_K_S	1.23 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.Q6_K.gguf	Q6_K	1.42 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.Q8_0.gguf	Q8_0	1.83 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.f16.gguf	F16	3.45 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ1_M.gguf	i1-IQ1_M	544 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ1_S.gguf	i1-IQ1_S	516 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ2_M.gguf	i1-IQ2_M	695 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ2_S.gguf	i1-IQ2_S	658 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ2_XS.gguf	i1-IQ2_XS	632 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ2_XXS.gguf	i1-IQ2_XXS	590 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ3_M.gguf	i1-IQ3_M	896 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ3_S.gguf	i1-IQ3_S	867 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ3_XS.gguf	i1-IQ3_XS	834 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ3_XXS.gguf	i1-IQ3_XXS	754 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ4_NL.gguf	i1-IQ4_NL	1.05 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-IQ4_XS.gguf	i1-IQ4_XS	1.01 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q2_K.gguf	i1-Q2_K	778 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q2_K_S.gguf	i1-Q2_K_S	733 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q3_K_L.gguf	i1-Q3_K_L	1 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q3_K_M.gguf	i1-Q3_K_M	940 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q3_K_S.gguf	i1-Q3_K_S	867 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q4_0.gguf	i1-Q4_0	1.06 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q4_1.gguf	i1-Q4_1	1.14 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q4_K_M.gguf	i1-Q4_K_M	1.11 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q4_K_S.gguf	i1-Q4_K_S	1.06 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q5_K_M.gguf	i1-Q5_K_M	1.26 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q5_K_S.gguf	i1-Q5_K_S	1.23 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.i1-Q6_K.gguf	i1-Q6_K	1.42 GB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.imatrix.gguf	imatrix	2.09 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.mmproj-Q8_0.gguf	mmproj-Q8_0	445 MB	Download
Qwen3-VL-2B-Instruct-abliterated-v1.mmproj-f16.gguf	mmproj-f16	819 MB	Download