Instructions to use olka-fi/Qwen3.5-27B-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use olka-fi/Qwen3.5-27B-MXFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="olka-fi/Qwen3.5-27B-MXFP4")
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("olka-fi/Qwen3.5-27B-MXFP4")
model = AutoModelForMultimodalLM.from_pretrained("olka-fi/Qwen3.5-27B-MXFP4", 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 olka-fi/Qwen3.5-27B-MXFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "olka-fi/Qwen3.5-27B-MXFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "olka-fi/Qwen3.5-27B-MXFP4",
		"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/olka-fi/Qwen3.5-27B-MXFP4

SGLang

How to use olka-fi/Qwen3.5-27B-MXFP4 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 "olka-fi/Qwen3.5-27B-MXFP4" \
    --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": "olka-fi/Qwen3.5-27B-MXFP4",
		"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 "olka-fi/Qwen3.5-27B-MXFP4" \
        --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": "olka-fi/Qwen3.5-27B-MXFP4",
		"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 olka-fi/Qwen3.5-27B-MXFP4 with Docker Model Runner:
```
docker model run hf.co/olka-fi/Qwen3.5-27B-MXFP4
```

Qwen3.5-27B-MXFP4

MXFP4 (E2M1) weight-only quantization of Qwen/Qwen3.5-27B.

Quantization Details

Format: MXFP4 (E2M1 float4), packed 2 values per uint8 byte
Scales: E8M0 per group of 32 elements
Method: 3-candidate MSE-optimal quantization (per-block best-fit)
Config: compressed-tensors with mxfp4-pack-quantized format

Layers NOT quantized

Attention, embeddings, LM head, gates, linear attention, and vision modules are kept in original precision:

re:.*self_attn.*, re:.*.mlp.gate$, re:.*lm_head.*,
re:.*embed_tokens.*, re:.*linear_attn.*, re:.*shared_expert_gate.*,
re:.*visual.*, re:.*mtp.*

Evaluation

Metric	Value
Perplexity (WikiText-2)	6.87
Context	max_len=2048, stride=512
Tokens scored	297,471

Usage with vLLM

from vllm import LLM, SamplingParams

llm = LLM(model="olka-fi/Qwen3.5-27B-MXFP4")
output = llm.generate("Hello, world!", SamplingParams(max_tokens=128))
print(output[0].outputs[0].text)

Quantization Tool

Quantized with qstream -- MXFP4 quantization toolkit for vLLM-compatible models.

Acknowledgments

Base model by Qwen Team.

Downloads last month: 81

Safetensors

Model size

20B params

Tensor type

BF16

F32

Model tree for olka-fi/Qwen3.5-27B-MXFP4

Base model

Qwen/Qwen3.5-27B

Quantized

(230)

this model