Instructions to use SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked with Transformers:

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

pipe = pipeline("image-text-to-text", model="SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked")
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("SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked")
model = AutoModelForMultimodalLM.from_pretrained("SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked", 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 SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked",
		"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/SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked

SGLang

How to use SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked 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 "SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked" \
    --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": "SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked",
		"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 "SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked" \
        --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": "SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked",
		"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 SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked with Docker Model Runner:
```
docker model run hf.co/SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked
```

Swiss-guy commited on 2 days ago

Commit

d1f61d6

verified ·

1 Parent(s): 1ef6e47

Link verified FP8 NVFP4 GPTQ and GGUF releases

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README.md +7 -0

README.md CHANGED Viewed

@@ -141,6 +141,13 @@ For low-latency direct answers, set `enable_thinking=False`. For reasoning-inten
 The exact direction bank and build provenance are included for internal reproducibility.
 ## Roadmap
 SwissNeuron is the base release in a broader model series. Planned follow-ups include:

 The exact direction bank and build provenance are included for internal reproducibility.
+## Quantized releases
+- [FP8 Dynamic](https://huggingface.co/SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked-FP8) — W8A8 compressed-tensors, verified Transformers generation
+- [NVFP4](https://huggingface.co/SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked-NVFP4) — ModelOpt W4A4 for NVIDIA Blackwell, verified on B300/SGLang
+- [GPTQ INT4](https://huggingface.co/SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked-GPTQ-INT4) — W4A16 group-size 128, verified Transformers generation
+- [GGUF](https://huggingface.co/SwissNeuron/Qwen3.8-27B-SwissNeuron-Derisked-GGUF) — native BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, BF16 vision mmproj, and BF16 MTP draft; LM Studio and Ollama compatible
 ## Roadmap
 SwissNeuron is the base release in a broader model series. Planned follow-ups include: