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

Libraries

How to use prithivMLmods/Qwen3-VL-8B-Thinking-c_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-Thinking-c_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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-8B-Thinking-c_abliterated-v2")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Qwen3-VL-8B-Thinking-c_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=256)
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-Thinking-c_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-Thinking-c_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-Thinking-c_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-Thinking-c_abliterated-v2

SGLang

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

You need to agree to share your Hugging Face information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Qwen3-VL-8B-Thinking-c_abliterated-v2

Qwen3-VL-8B-Thinking-c_abliterated-v2 is the high-reasoning successor to the abliterated-v1 series. This model implements Continual Abliteration (c_abliterated) —a specialized training regimen that applies successive iterations of refusal-neutralization. By building on the foundation of Qwen3-VL-8B-Thinking-abliterated-v1, this version is engineered to provide deep, Chain-of-Thought (CoT) style reasoning and uncensored visual analysis for the most complex multimodal tasks.

Key Highlights

Continual Abliteration (c_abliterated): Specifically refined through the iterative removal of refusal weights found in the v1 predecessor, ensuring a seamless instruction-following experience without safety-trigger interference.
Deep Thinking Architecture: Optimized for "Thinking" workflows, providing structured internal reasoning before delivering final captions or analysis.
8B Parameter Logic: Offers the sophisticated linguistic and visual comprehension required for technical, medical, forensic, and abstract datasets.
Zero-Refusal Captioning: Bypasses conventional content filters to provide objective, factual descriptions of sensitive or nuanced visual content.
Multi-Aspect Ratio Intelligence: Native support for varied resolutions, allowing the model to maintain context and spatial reasoning across panoramic or vertical imagery.

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-Thinking-abliterated.

Quick Start with Transformers

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

# Load the v2 Thinking c_abliterated model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Qwen3-VL-8B-Thinking-c_abliterated-v2",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen3-VL-8B-Thinking-c_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 reasoning-based caption 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")

# Thinking models often benefit from higher token limits for internal reasoning
generated_ids = model.generate(**inputs, max_new_tokens=512)

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

Iterative Safety Research: Analyzing how "Continual Abliteration" affects the model's internal logic gates compared to the v1 release.
Unfiltered Visual Reasoning: High-depth analysis of images where objective truth is required over curated safety responses.
Red-Teaming & Stress Testing: Testing the robustness of vision-language systems in identifying edge-case scenarios.
Creative Dataset Curation: Generating rich, "thinking-first" metadata for artistic and complex visual libraries.

Limitations & Risks

Critical Note: This model is a c_abliterated variant and does not follow standard safety guardrails.

Content Sensitivity: Will generate descriptive text for explicit or sensitive visuals if prompted.
Reasoning Latency: Due to the "Thinking" nature of the model, outputs may be longer as the model processes internal reasoning steps.
Environment: Intended strictly for research, ethical red-teaming, and professional environments.