Instructions to use Minachist/Qwen3.6-27B-INT8-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Minachist/Qwen3.6-27B-INT8-AutoRound with Transformers:

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

pipe = pipeline("image-text-to-text", model="Minachist/Qwen3.6-27B-INT8-AutoRound")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Minachist/Qwen3.6-27B-INT8-AutoRound")
model = AutoModelForImageTextToText.from_pretrained("Minachist/Qwen3.6-27B-INT8-AutoRound")
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

vLLM

How to use Minachist/Qwen3.6-27B-INT8-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Minachist/Qwen3.6-27B-INT8-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Minachist/Qwen3.6-27B-INT8-AutoRound",
		"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/Minachist/Qwen3.6-27B-INT8-AutoRound

SGLang

How to use Minachist/Qwen3.6-27B-INT8-AutoRound 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 "Minachist/Qwen3.6-27B-INT8-AutoRound" \
    --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": "Minachist/Qwen3.6-27B-INT8-AutoRound",
		"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 "Minachist/Qwen3.6-27B-INT8-AutoRound" \
        --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": "Minachist/Qwen3.6-27B-INT8-AutoRound",
		"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 Minachist/Qwen3.6-27B-INT8-AutoRound with Docker Model Runner:
```
docker model run hf.co/Minachist/Qwen3.6-27B-INT8-AutoRound
```

Qwen3.6-27B-INT8-AutoRound / quantize.py

Minachist

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	import torch
	from datasets import load_dataset
	from transformers import AutoTokenizer
	from auto_round import AutoRound

	model_name_or_path = "."
	output_dir = "./Qwen3.6-27B-INT8-autoround"

	tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)

	ignore_keywords =[
	"embed_tokens",
	"linear_attn",
	# "self_attn",
	"visual",
	"mtp",
	"lm_head"
	]

	layer_config = {}
	for keyword in ignore_keywords:
	layer_config[keyword] = {"bits": 16}

	hf_dataset = load_dataset("NeelNanda/pile-10k", split="train")

	seqlen = 2048
	tokens_list =[]

	max_samples = 512

	for item in hf_dataset:
	text = item["text"]

	tokenized = tokenizer(
	text,
	truncation=True,
	max_length=seqlen,
	return_tensors="pt"
	)

	input_ids = tokenized["input_ids"]

	if input_ids.shape[-1] >= seqlen:
	tokens_list.append(input_ids)

	if len(tokens_list) >= max_samples:
	break


	ar = AutoRound(
	model=model_name_or_path,
	tokenizer=tokenizer,
	scheme="W8A16",
	enable_torch_compile=True,
	group_size=-1,
	sym=True,
	layer_config=layer_config,

	dataset=tokens_list,
	device_map="0,1",
	batch_size=8,
	seqlen=seqlen,
	iters=1000
	)

	ar.quantize_and_save(output_dir, format="auto_round")