Instructions to use Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw with Transformers:

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

pipe = pipeline("image-text-to-text", model="Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw")
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("Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw")
model = AutoModelForMultimodalLM.from_pretrained("Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw", 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 Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw",
		"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/Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw

SGLang

How to use Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw 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 "Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw" \
    --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": "Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw",
		"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 "Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw" \
        --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": "Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw",
		"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 Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw with Docker Model Runner:
```
docker model run hf.co/Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw
```

Qwen3.8-27B-EXL3 2.0bpw (Mirror)

⚠️ This is a mirror repository. All credit for the quantization goes to turboderp.

Original repository: turboderp/Qwen3.8-27B-exl3 (branch: SC_2.00bpw_H3_V3)

This repo exists solely to provide an alternative download source. I did not create this quant — it is an exact copy of turboderp's work.

Model Details

Base model: Qwen/Qwen3.8-27B
Quantization: EXL3, 2.0 bits per weight (SC_2.00bpw_H3_V3)
Quantized by: turboderp
Format: EXL3 (ExLlamaV3)
Size: ~9.7 GB

About

This is an EXL3 quantization of Qwen3.8-27B, a native vision-language model with 27B parameters. The quantization was performed by turboderp using the ExLlamaV3 framework.

For usage instructions, benchmarks, and technical details, please refer to the original repository.

Usage

Use with ExLlamaV3 or compatible inference engines (e.g., text-generation-webui with EXL3 support).

License

Apache-2.0 (inherited from the base model)

Downloads last month: 509

Safetensors

Model size

5B params

Tensor type

BF16

F16

I16