Instructions to use nDimensional/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nDimensional/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK with Transformers:

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

pipe = pipeline("image-text-to-text", model="nDimensional/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK")
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("nDimensional/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK")
model = AutoModelForMultimodalLM.from_pretrained("nDimensional/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK", 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 nDimensional/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nDimensional/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK"
# 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/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK",
		"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/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK

SGLang

How to use nDimensional/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK 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/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK" \
    --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/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK",
		"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/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK" \
        --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/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK",
		"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/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK with Docker Model Runner:
```
docker model run hf.co/nDimensional/Qwen3.5-35B-A3B-Uncensored-FP8_BLOCK
```

Qwen3.5-35B-A3B Uncensored (FP8_BLOCK)

A safetensors conversion and quantization of HauhauCS/Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive (GGUF).

Model Details


Architecture	Qwen3.5 MoE hybrid attention (30 GDN + 10 full standard attention layers)
Parameters	35B-A3B
Base model	Qwen/Qwen3.5-35B-A3B
Source GGUF	HauhauCS/Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive
Format	BF16 (Mixed/Compressed)
Quantization	`FP8_BLOCK` applied to Linear transformer layers.
Stripped layers	Multi-Token Prediction (MTP) due to original HF -> GGUF conversion.
Conversion type	Lossless GGUF to safetensors conversion + merge with base model vision layers + Block-wise quantization
Unquantized weights	Coming Soon

Conversion Details

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

The vision encoder weights are copied directly from the official Qwen/Qwen3.5-35B-A3B base model, after confirming the vision encoder (mmproj) was not modified in the source GGUF.

Next, the linear weights of the transformer blocks were quantized to F8_E4M3 using llm-compressor.

Test Inference Details

1x A100 (80GB)
Python 3.12
vllm & transformers version:
- transformers 5.5.0
- vllm nightly (latest commit tested: 7b743ba)
vLLM online serve flags:
- --quantization compressed-tensors
- --max-model-len 16384
- --gpu-memory-utilization 0.9140 with VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=1 environmental variable
- --limit-mm-per-prompt.image 4
- --enable-prefix-caching
- --enable-expert-parallel
- --reasoning-parser qwen3
- --default-chat-template-kwargs {"enable_thinking": false} disabled thinking/reasoning for vllm>=0.18.1
- Note: Used for batch image captioning tests.