Instructions to use SurgeFF/AriannaV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SurgeFF/AriannaV1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="SurgeFF/AriannaV1")
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("SurgeFF/AriannaV1")
model = AutoModelForMultimodalLM.from_pretrained("SurgeFF/AriannaV1", 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 SurgeFF/AriannaV1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SurgeFF/AriannaV1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SurgeFF/AriannaV1",
		"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/SurgeFF/AriannaV1

SGLang

How to use SurgeFF/AriannaV1 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 "SurgeFF/AriannaV1" \
    --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": "SurgeFF/AriannaV1",
		"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 "SurgeFF/AriannaV1" \
        --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": "SurgeFF/AriannaV1",
		"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 SurgeFF/AriannaV1 with Docker Model Runner:
```
docker model run hf.co/SurgeFF/AriannaV1
```

AriannaV1

All-in-one local assistant model for Surge's home AI fleet. AriannaV1 is gemma-4-12b-it (multimodal, encoder-free) with the Aria adapter-v17 LoRA merged into the base weights — a single standalone model, not a bare adapter.

Architecture

Arianna's weights carry the core: text, reasoning, identity, memory, math, code, grammar, storytelling, tool-selection, safety, and vision-/audio-understanding. Every other modality is a sidecar the core orchestrates, never baked into the weights: realtime voice (STT/TTS), video generation, image generation, and retrieval embeddings. The AgentOS runtime — self-improvement, self-healing, toolsmith, curiosity, semantic memory, append-only event log, reflection — wraps this core as the self-modifying brain.

Gated eval scores (v17, suite fingerprint `2be660e12d96bc71`)

capability	score
math (100-item GSM8K held-out)	0.90
tools	1.00
identity	0.90
identity_bare (monitor-only)	0.625
memory	0.90

Disjoint 150-problem math confirmation: 140/150 = 93.3%.

Files

Repo root — merged standalone weights (AutoModelForImageTextToText.from_pretrained just works).
adapter/ — the original Aria v17 LoRA, to stack on the base yourself.

License

Gemma license, inherited from the base model. Built by Sergio Williams / Surge.

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for SurgeFF/AriannaV1

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Finetuned

unsloth/gemma-4-12b-it

Finetuned

(38)

this model

AriannaV1

Architecture

Gated eval scores (v17, suite fingerprint 2be660e12d96bc71)

Files

License

Model tree for SurgeFF/AriannaV1

Gated eval scores (v17, suite fingerprint `2be660e12d96bc71`)