Instructions to use nDimensional/Qwen3.5-9B-Uncensored-Safetensors with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nDimensional/Qwen3.5-9B-Uncensored-Safetensors with Transformers:

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

pipe = pipeline("image-text-to-text", model="nDimensional/Qwen3.5-9B-Uncensored-Safetensors")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("nDimensional/Qwen3.5-9B-Uncensored-Safetensors")
model = AutoModelForImageTextToText.from_pretrained("nDimensional/Qwen3.5-9B-Uncensored-Safetensors")
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 nDimensional/Qwen3.5-9B-Uncensored-Safetensors with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nDimensional/Qwen3.5-9B-Uncensored-Safetensors"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nDimensional/Qwen3.5-9B-Uncensored-Safetensors",
		"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/nDimensional/Qwen3.5-9B-Uncensored-Safetensors

SGLang

How to use nDimensional/Qwen3.5-9B-Uncensored-Safetensors 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 "nDimensional/Qwen3.5-9B-Uncensored-Safetensors" \
    --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": "nDimensional/Qwen3.5-9B-Uncensored-Safetensors",
		"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 "nDimensional/Qwen3.5-9B-Uncensored-Safetensors" \
        --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": "nDimensional/Qwen3.5-9B-Uncensored-Safetensors",
		"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 nDimensional/Qwen3.5-9B-Uncensored-Safetensors with Docker Model Runner:
```
docker model run hf.co/nDimensional/Qwen3.5-9B-Uncensored-Safetensors
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.5-9B Uncensored (Safetensors)

A safetensors conversion of HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive (GGUF) to work with frameworks that don't support GGUF or split GGUF format (vLLM, transformers, etc..).

Note for vLLM users: As of vLLM 0.18.1, reasoning is enabled by default for qwen3 family of models. To disable add --default-chat-template-kwargs '{"enable_thinking": false}' flag to your serve startup; otherwise leave default to keep reasoning enabled.

Model Details


Architecture	Qwen3.5 dense hybrid attention (24 GDN + 8 full standard attention layers)
Parameters	9B
Base model	Qwen/Qwen3.5-9B
Source GGUF	HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive
Format	BF16
Conversion type	Lossless GGUF to safetensors conversion + merge with base model vision layers.

Conversion Details

Converted using code release coming soon, which reverses transforms applied during HF -> GGUF conversion.

The vision encoder weights are copied directly from the official Qwen/Qwen3.5-9B base model, after confirming the vision encoder (mmproj) was not modified in the source GGUF.

Config Changes from Base Model

Field	Base Model	This Model	Reason
`mtp_num_hidden_layers`	`1`	`0`	Multi-Token Prediction (MTP) weights are stripped during GGUF export; disabled to prevent missing-tensor errors

All other config fields are identical to the official Qwen3.5-9B.

Tensor Analysis

Every converted tensor was compared against the official Qwen3.5-9B weights to verify transform conversiion correctness and characterize the moddifications made by the GGUF author. Since this is a BF16 -> BF16 conversion, after correcting transforms errors from GGUF noise; any non-zero difference represents an intentional modification to the weights.

Spot-check Value Comparison Findings (Layer 0 + Layer 3)

Tensor	Max Diff	Mean Diff	Status
`self_attn.o_proj.weight`	5.18e-02	6.51e-04	⚠️ Modified
`linear_attn.norm.weight`	1.95e-03	9.75e-04	⚠️ Modified

Note: The spot-check only covers layers 0 and 3. A full per-layer analysis across all 32 layers was not needed for purpose of conversion verification. Other layers may contain similar weight modifications.

What's NOT Modified

Vision encoder: all 3333 model.visual.* tensors are copied directly from the official Qwen3.5-9B model.
Tokenizer: identical vocab, merges, and special tokens.
Embeddings: embed_tokens.weight and lm_head.weight are bit-identical.
MLP layers: all gate_proj, up_proj, down_proj weights checked are bit-identical.