Instructions to use dudeman2512/Qwen3.8-27B-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dudeman2512/Qwen3.8-27B-int4 with Transformers:

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

pipe = pipeline("text-generation", model="dudeman2512/Qwen3.8-27B-int4")
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("dudeman2512/Qwen3.8-27B-int4")
model = AutoModelForMultimodalLM.from_pretrained("dudeman2512/Qwen3.8-27B-int4", 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 dudeman2512/Qwen3.8-27B-int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dudeman2512/Qwen3.8-27B-int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dudeman2512/Qwen3.8-27B-int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dudeman2512/Qwen3.8-27B-int4

SGLang

How to use dudeman2512/Qwen3.8-27B-int4 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 "dudeman2512/Qwen3.8-27B-int4" \
    --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": "dudeman2512/Qwen3.8-27B-int4",
		"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 "dudeman2512/Qwen3.8-27B-int4" \
        --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": "dudeman2512/Qwen3.8-27B-int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dudeman2512/Qwen3.8-27B-int4 with Docker Model Runner:
```
docker model run hf.co/dudeman2512/Qwen3.8-27B-int4
```

Qwen3.8-27B-int4

int4 quantization of Qwen/Qwen3.8-27B, produced with compressed-tensors by streaming the checkpoint tensor-by-tensor (the model is never fully instantiated).

4-bit integer weights in groups of 128 with a BF16 scale. Smallest of the set and the highest error.

All quantizations of this model

Variant	Format	Size	vs BF16	Mean rel. error	Linears quantized	Left BF16
Qwen3.8-27B-FP8	`float-quantized`	30.35 GB	55%	0.0264	615	0
Qwen3.8-27B-NVFP4	`nvfp4-pack-quantized`	19.29 GB	35%	0.0950	615	0
Qwen3.8-27B-int4 ← this one	`pack-quantized`	18.31 GB	33%	0.1178	588	27

Mean relative error is ||dequant(W) - W|| / ||W||, averaged over a sample of quantized Linear layers, measured against the original BF16 weights. Lower is better.

This variant


Format	`pack-quantized`
Weight bits	4
Group size	128
Strategy	`group`
Linears quantized	588
Left in BF16	27
Shards	5
On disk	18.31 GB
Mean relative error	0.1178
Shape/dtype conformance failures	0

Layers left in BF16

27 Linear layers could not be quantized at group size 128: their in_features is not a multiple of 128. They are stored as BF16 and listed in the ignore list of config.json, so servers load them unquantized rather than misreading them.

They are all in the vision tower, so the language model is fully quantized:

model.visual.blocks.0.mlp.linear_fc2
model.visual.blocks.1.mlp.linear_fc2
model.visual.blocks.10.mlp.linear_fc2
model.visual.blocks.11.mlp.linear_fc2
model.visual.blocks.12.mlp.linear_fc2
model.visual.blocks.13.mlp.linear_fc2
... and 21 more

Use with vLLM

vllm serve dudeman2512/Qwen3.8-27B-int4

How this was made

Every produced tensor is checked for shape/dtype conformance against what the server expects, then reconstruction error is measured against the source BF16 weights, before anything is published. The numbers in the table above are those measurements — not estimates.

Downloads last month: 32

Safetensors

Model size

28B params

Tensor type

BF16

I32

Model tree for dudeman2512/Qwen3.8-27B-int4

Base model

Qwen/Qwen3.8-27B

Quantized

(691)

this model