Instructions to use sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta with Transformers:

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

pipe = pipeline("image-text-to-text", model="sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta")
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("sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta")
model = AutoModelForMultimodalLM.from_pretrained("sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta")
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 sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta",
		"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/sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta

SGLang

How to use sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta 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 "sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta" \
    --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": "sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta",
		"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 "sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta" \
        --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": "sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta",
		"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 sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta with Docker Model Runner:
```
docker model run hf.co/sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-VL-30B-A3B-Instruct — Sovereign Abliterated (Beta)

Abliterated version of Qwen/Qwen3-VL-30B-A3B-Instruct with safety alignment removed using the Sovereign abliteration framework.

Performance

Metric	Value
Refusals	2/100 (98% ASR)
KL Divergence	0.0147
Capability Degradation	0.0%
Optimization Trials	50+ (trial #12 selected)

Method: Sovereign Framework

Sovereign is a multi-directional abliteration framework combining several techniques:

SOM Multi-Direction Extraction: Self-Organizing Maps extract 24 independent refusal directions per layer (vs 1 for standard abliteration)
Norm-Preserving Biprojected Ablation: Preserves weight row norms exactly, preventing reasoning degradation (Lai/grimjim 2025)
3-Objective Bayesian Optimization: Optimizes KL divergence, refusal rate, and capability preservation simultaneously via Optuna TPE
CIR Protected Subspace: Projects refusal directions away from common capability representations to prevent collateral damage
Quantization-Aware Ablation: Ensures edits survive GGUF Q4 export
SteerMoE Expert Dampening: Dampens MoE experts that specialize in refusal behavior

Key Hyperparameters (Trial #12)

Component	Peak Weight	Peak Layer	Falloff
attn.o_proj	3.45	38.2	2.0 layers
attn.q_proj	1.82	38.0	25.8 layers
attn.k_proj	5.71	31.5	2.6 layers
attn.v_proj	2.66	33.5	2.7 layers
mlp.down_proj	4.51	29.7	3.8 layers

Direction index: 41.9 (global), 24 SOM directions with per-direction weights.

Model Details

Property	Value
Architecture	`Qwen3VLMoeForConditionalGeneration`
Parameters	30B total, ~3B active per token
Layers	48
Hidden size	2048
Experts	128 (8 active per token)
Vision	27-layer ViT + DeepStack hierarchical fusion
Dtype	BF16
License	Apache 2.0

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained(
    "sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(
    "sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta",
    trust_remote_code=True,
)

Comparison

Model	ASR	Refusals	KL Divergence
Base (Qwen3-VL-30B-A3B-Instruct)	0.0%	100/100	0.0000
huihui-ai abliterated	66.0%	34/100	0.2619
Sovereign beta (this model)	98.0%	2/100	0.0147

Support This Work

If you find this model useful, please consider supporting my work:

Buy me a coffee on Ko-fi — Your donations help fund GPU time and continued development of the Sovereign abliteration framework. Every contribution helps!

Disclaimer

This model is provided for research purposes. The removal of safety alignment means the model may produce content that the original model would refuse. Users are responsible for ensuring appropriate use.

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Safetensors

Model size

31B params

Tensor type

BF16

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