Instructions to use realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6 with Transformers:

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

pipe = pipeline("image-text-to-text", model="realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6")
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("realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6")
model = AutoModelForMultimodalLM.from_pretrained("realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6", 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 realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6",
		"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/realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6

SGLang

How to use realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6 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 "realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6" \
    --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": "realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6",
		"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 "realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6" \
        --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": "realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6",
		"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 realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6 with Docker Model Runner:
```
docker model run hf.co/realderpz/Swift-1.5-Qwen3.8-27B-PARO-MXFP6
```

Swift-1.5-Qwen3.8-27B-PARO-MXFP6

File size: 1,133 Bytes

2add9d7

Swift 1.5 Qwen3.8-27B
Copyright 2026 UkisAI

UkisAI's contribution (the "Swift Contribution") is licensed under the
Swift Open License v1.0. See LICENSE.

This model is a Derivative Work of Qwen3.8-27B
  https://huggingface.co/Qwen/Qwen3.8-27B
  Copyright 2026 Alibaba Cloud
  Licensed under the Apache License, Version 2.0. See LICENSE-APACHE-2.0.

Swift 1.5 continues UkisAI's Swift 1.0 model, itself a Qwen3.8-27B
derivative. The full release incorporates the Qwen3.8-27B Base Model and
UkisAI's Swift 1.0 and Swift 1.5 contributions.

Changes made by UkisAI (Apache License 2.0, Section 4(b) change notice):
  - model-*.safetensors, model.safetensors.index.json: the model weights from
    Swift 1.0 were further adapted by UkisAI using additional post-training
    methods.
  - README.md: replaced. LICENSE and NOTICE added.
  - All other files (config.json, generation_config.json, chat_template.jinja,
    tokenizer.json, tokenizer_config.json, vocab.json, merges.txt,
    preprocessor_config.json, video_preprocessor_config.json) are retained from
    the parent model and remain available under Apache License, Version 2.0.