Instructions to use philbert440/Qwen3.8-27B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use philbert440/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="philbert440/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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("philbert440/Qwen3.8-27B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("philbert440/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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use philbert440/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 "philbert440/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": "philbert440/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/philbert440/Qwen3.8-27B-NVFP4

SGLang

How to use philbert440/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 "philbert440/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": "philbert440/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 "philbert440/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": "philbert440/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 philbert440/Qwen3.8-27B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/philbert440/Qwen3.8-27B-NVFP4
```

Qwen3.8-27B-NVFP4 / recipe.yaml

philbert440

v2 weights: bake-off winner recipe C (GDN in_proj_qkv/z BF16, 768 CoT + 256 wiki calib, act-order + MSE); V100-validated; README v2

8214a60 verified about 2 months ago

Raw History Blame Contribute Delete

10.7 kB

	default_stage:
	default_modifiers:
	GPTQModifier:
	config_groups:
	group_0:
	targets: [Linear]
	weights:
	num_bits: 4
	type: float
	symmetric: true
	group_size: 16
	strategy: tensor_group
	block_structure: null
	dynamic: false
	actorder: weight
	scale_dtype: null
	zp_dtype: null
	observer: mse
	observer_kwargs: {}
	input_activations: null
	output_activations: null
	format: null
	targets: [Linear]
	ignore: [model.visual.blocks.0.attn.qkv, model.visual.blocks.0.attn.proj, model.visual.blocks.0.mlp.linear_fc1,
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	model.language_model.layers.62.linear_attn.in_proj_a, lm_head, mtp.fc, 're:.*linear_attn\.in_proj_qkv$',
	're:.*linear_attn\.in_proj_z$']
	bypass_divisibility_checks: false
	requires_calibration_data: true
	block_size: 128
	dampening_frac: 0.01
	actorder: weight
	offload_hessians: false