Instructions to use prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2"
# 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-8B-Instruct-abliterated-v2",
		"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-8B-Instruct-abliterated-v2

SGLang

How to use prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2 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-8B-Instruct-abliterated-v2" \
    --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-8B-Instruct-abliterated-v2",
		"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-8B-Instruct-abliterated-v2" \
        --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-8B-Instruct-abliterated-v2",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-VL-8B-Instruct-abliterated-v2.0

Qwen3-VL-8B-Instruct-abliterated-v2.0 is an abliterated (v2.0) variant of Qwen3-VL-8B-Instruct, designed for Abliterated Reasoning and Captioning. This model is fine-tuned to produce highly detailed, descriptive, and reasoning-focused outputs across a wide range of visual and multimodal contexts, including complex, sensitive, or nuanced content. It supports varied image resolutions and aspect ratios while maintaining interpretive coherence and descriptive accuracy.

Key Highlights

Abliterated / Uncensored Captioning Fine-tuned to bypass conventional content filters while preserving factual, descriptive, and reasoning-rich outputs.
High-Fidelity Reasoning and Descriptions Generates in-depth captions and reasoning for general, artistic, technical, abstract, and low-context images.
Robust Across Aspect Ratios Performs consistently on wide, tall, square, panoramic, and irregular image dimensions.
Variational Detail Control Capable of generating outputs ranging from concise summaries to intricate, multi-level descriptive reasoning.
Foundation on Qwen3-VL-8B-Instruct Architecture Built upon Qwen3-VL-8B-Instruct’s multimodal reasoning, comprehension, and instruction-following framework.
Multilingual Output Capability Primarily outputs in English, but adaptable to multiple languages via prompt engineering.

Base Model Signatures:

This model has been re-sharded and optimized for the latest Transformers version from the base model: https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated.

Quick Start with Transformers

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v2")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Provide a detailed caption and reasoning for this image."},
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)

inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
).to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=128)

generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]

output_text = processor.batch_decode(
    generated_ids_trimmed,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text)

Intended Use

This model is suited for:

Generating detailed, unfiltered captions and reasoning for general-purpose and artistic datasets.
Research in content moderation, red-teaming, and generative safety analysis.
Enabling descriptive captioning and reasoning for datasets typically excluded from mainstream models.
Creative and exploratory applications such as storytelling, visual interpretation, and multimodal reasoning.
Captioning and reasoning for non-standard, stylized, or abstract visual content.

Limitations

May generate explicit, sensitive, or offensive content depending on the prompt and input image.
Not suitable for production environments that require strict content filtering or moderation.
Output tone, style, and reasoning depth can vary depending on phrasing and visual complexity.
May show variability in performance on synthetic or highly abstract visuals.