Instructions to use AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4")
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("AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4")
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 AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4

SGLang

How to use AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4 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 "AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4" \
    --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": "AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4" \
        --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": "AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4 with Docker Model Runner:
```
docker model run hf.co/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4
```

AEON-7 commited on 7 days ago

Commit

77e1418

verified ·

1 Parent(s): c2ebb91

Doc: validated on unified aeon-vllm-ultimate:latest base (2026-06-09 fleet check)

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@@ -31,6 +31,8 @@ tags:
 # Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4
 > **Deployment, operations & benchmarks → [github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-DFlash](https://github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-DFlash)**
 >
 > The GitHub repo is the source of truth for the production deployment guide, hardware-tuned docker-compose configs, full configuration reference, measured throughput benchmarks (32 tok/s median / 56 tok/s peak / 350 ms TTFT on DGX Spark), and `AGENTS.md` — an operator's manual that pre-empts common stale-documentation traps for AI coding agents working on this stack.

 # Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4
+> ✅ **Validated 2026-06-09 on the unified [AEON vLLM Ultimate image](https://github.com/AEON-7/vllm-ultimate-dgx-spark) `ghcr.io/aeon-7/aeon-vllm-ultimate:latest`** (vLLM 0.22.1+pr44389) — loads + serves cleanly (compressed-tensors) with the z-lab DFlash drafter @ n=12 — measured **33.2 tok/s single / 154.4 tok/s conc×16**, 28% DFlash acceptance. Recommended container base.
 > **Deployment, operations & benchmarks → [github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-DFlash](https://github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-DFlash)**
 >
 > The GitHub repo is the source of truth for the production deployment guide, hardware-tuned docker-compose configs, full configuration reference, measured throughput benchmarks (32 tok/s median / 56 tok/s peak / 350 ms TTFT on DGX Spark), and `AGENTS.md` — an operator's manual that pre-empts common stale-documentation traps for AI coding agents working on this stack.