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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="Qwen/Qwen3.8-27B")
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("Qwen/Qwen3.8-27B")
model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3.8-27B", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qwen/Qwen3.8-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen3.8-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen3.8-27B",
		"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/Qwen/Qwen3.8-27B

SGLang

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

Please release the 35b or 70b version

by NjProVk - opened 9 days ago

Discussion

NjProVk

9 days ago

Please release the 35b or 70b moe version

Amantt

9 days ago

35B-A3B please (or similar MoE)

CYISNOTHERE

9 days ago

Both if you have the budget, 35B-A3B if you can fit one more please 🙏

mslxd

9 days ago

铜球 35B-A3B

aabbt

9 days ago

27B is awsome. May i expect 35B-A3B?

coe-arrtificial

9 days ago

35B-A3B would be just perfect fit for rest of market. All the Spark, Mac and Strix users will love that!

TeAmErX

9 days ago

70b would be fire

anjeysapkovski

9 days ago

35B A3B would be very appreciable.

funnygeeker

9 days ago

我太需要Qwen3.8-35B-A3B了，感谢🙏

vimmercodex

9 days ago

35B A3B
Letss goo!

mercurynomercy

9 days ago

35b please 🥲

mung90

8 days ago

•

edited 8 days ago

There are evidences:

https://www.reddit.com/r/Qwen_AI/comments/1vot19y/do_you_guys_think_there_is_a_chance_qwen_makes_a/

funnygeeker

6 days ago

Unfortunately, according to the commit I observed yesterday, this commit was subsequently removed and described as a wrong commit. (It was removed in subsequent commits.)

However, based on the historical release record of Qwen, it is still possible that it will be released on August 20(And this is before the release, you may not receive any notification until after the release.).

If there is still no release after the 26th, the probability of release will become very low.

CYISNOTHERE

6 days ago

Unfortunately, according to the commit I observed yesterday, this commit was subsequently removed and described as a wrong commit. (It was removed in subsequent commits.)

However, based on the historical release record of Qwen, it is still possible that it will be released on August 20(And this is before the release, you may not receive any notification until after the release.).

If there is still no release after the 26th, the probability of release will become very low.

That would be a betrayal if they didn't 🥲

mung90

2 days ago

there are showing up here in hf whittle moe from the 27b (https://huggingface.co/mradermacher/Qwen3.8-Whittle-MoE-27B-A17.8B-GGUF), because it's almost impossible for the most part of the users use the 27b for long context/agentic session where you need at least 100/150k ctx size. unsless you owe a 15/20k server with plenty of gpu...

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