Instructions to use a2genesis/Qwen3.8-27B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use a2genesis/Qwen3.8-27B-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="a2genesis/Qwen3.8-27B-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("a2genesis/Qwen3.8-27B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("a2genesis/Qwen3.8-27B-NVFP4", 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 a2genesis/Qwen3.8-27B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "a2genesis/Qwen3.8-27B-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": "a2genesis/Qwen3.8-27B-NVFP4",
		"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/a2genesis/Qwen3.8-27B-NVFP4

SGLang

How to use a2genesis/Qwen3.8-27B-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 "a2genesis/Qwen3.8-27B-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": "a2genesis/Qwen3.8-27B-NVFP4",
		"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 "a2genesis/Qwen3.8-27B-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": "a2genesis/Qwen3.8-27B-NVFP4",
		"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 a2genesis/Qwen3.8-27B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/a2genesis/Qwen3.8-27B-NVFP4
```

Qwen3.8-27B — NVFP4 (ModelOpt)

NVFP4 quantization of Qwen/Qwen3.8-27B, produced with NVIDIA TensorRT Model Optimizer 0.45 using NVIDIA's own recipe for the qwen3_5 family (huggingface/qwen3_5/ptq/w4a16_nvfp4-fp8_attn-kv_fp8_cast) — the same mixed-precision scheme NVIDIA used for its official Qwen3.6-27B NVFP4 release:

Component	Precision
MLP projections (gate/up/down) + `lm_head`	NVFP4 (W4A16, weight-only, block size 16)
Self-attention and linear-attention (GDN) projections	FP8 (E4M3, weights + activations)
KV cache	FP8 (constant amax)
Vision tower, MTP head, conv/gating layers	BF16 (unquantized)

Checkpoint size is ~21 GB (vs. ~55 GB BF16).

Calibration

Post-training quantization with 1024 samples (max. 512 tokens each), 256 each from four openly available datasets: cnn_dailymail, Magpie-Align/Magpie-Pro-MT-300K-v0.1, nvidia/OpenCodeReasoning, nvidia/OpenMathReasoning. Attention implementation during calibration: SDPA.

Serving with vLLM

vllm serve <this-repo> \
  --quantization modelopt \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --enable-auto-tool-choice

NVFP4 runs natively on Blackwell GPUs; the checkpoint loads on earlier architectures via vLLM's ModelOpt support with reduced benefit.

Notes

Community quantization by A2Genesis — not affiliated with or endorsed by the Qwen team or NVIDIA.
The MTP (multi-token prediction) tensors are preserved in BF16, so speculative decoding remains available.
License: Apache 2.0, inherited from the base model. All credit for the model itself belongs to the Qwen team.

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Safetensors

Model size

18B params

Tensor type

BF16

F8_E4M3

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Qwen/Qwen3.8-27B

Quantized

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