Instructions to use sionic-ai/Wasabi-30B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sionic-ai/Wasabi-30B with Transformers:

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

pipe = pipeline("image-text-to-text", model="sionic-ai/Wasabi-30B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("sionic-ai/Wasabi-30B")
model = AutoModelForMultimodalLM.from_pretrained("sionic-ai/Wasabi-30B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sionic-ai/Wasabi-30B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sionic-ai/Wasabi-30B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sionic-ai/Wasabi-30B",
		"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/sionic-ai/Wasabi-30B

SGLang

How to use sionic-ai/Wasabi-30B 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 "sionic-ai/Wasabi-30B" \
    --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": "sionic-ai/Wasabi-30B",
		"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 "sionic-ai/Wasabi-30B" \
        --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": "sionic-ai/Wasabi-30B",
		"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 sionic-ai/Wasabi-30B with Docker Model Runner:
```
docker model run hf.co/sionic-ai/Wasabi-30B
```

Wasabi-30B

File size: 1,237 Bytes

Wasabi-30B
Copyright 2026 Sionic AI Inc.

Wasabi-30B is released under the GNU Affero General Public License v3.0 (see LICENSE).

This model is a fine-tune of Muse-Glimmer-30B (https://huggingface.co/meta-models/Muse-Glimmer-30B,
revision 97c77dff50b2797bcc558fa2d909761dbc575c59), licensed under the Apache License, Version 2.0
(notices/Muse-Glimmer-30B-LICENSE.txt). The base model snapshot has no NOTICE file. Modification: the base
weights were fine-tuned with LoRA for Japanese page transcription and the adapter was merged into BF16 weights.
The base model's publisher also publishes a usage policy, reproduced in notices/Muse-Glimmer-30B-USAGE_POLICY.md.

The page reader wasabi_read (orientation check, UVDoc flattening, whole-page read and per-page output) is released
under the GNU Affero General Public License v3.0.

UVDoc (https://github.com/tanguymagne/UVDoc), code at commit 4c9b82b537057aff2526e6dd118a847cdd072e82: MIT License,
Copyright (c) 2023 Tanguy MAGNE. wasabi_read downloads the UVDoc code and its pretrained weights
(model/best_model.pkl) from that commit at first use and does not redistribute them. The UVDoc repository carries
no weights-specific licence statement; the weights remain under their own terms.