Instructions to use Surpem/Supertron3-0.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Surpem/Supertron3-0.8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Surpem/Supertron3-0.8B")
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("Surpem/Supertron3-0.8B")
model = AutoModelForMultimodalLM.from_pretrained("Surpem/Supertron3-0.8B", 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 Surpem/Supertron3-0.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Surpem/Supertron3-0.8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Surpem/Supertron3-0.8B",
		"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/Surpem/Supertron3-0.8B

SGLang

How to use Surpem/Supertron3-0.8B 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 "Surpem/Supertron3-0.8B" \
    --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": "Surpem/Supertron3-0.8B",
		"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 "Surpem/Supertron3-0.8B" \
        --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": "Surpem/Supertron3-0.8B",
		"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 Surpem/Supertron3-0.8B with Docker Model Runner:
```
docker model run hf.co/Surpem/Supertron3-0.8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Supertron3-0.8B: Edge Foundation Model for Tool Calling and Computer Use Agents

Model Description

Supertron3-0.8B is a compact Vision-Language Model (VLM) purpose-built for GUI Agents and agentic tool calling at the edge. It operates across diverse digital environments — web, desktop, and CLI — by interpreting visual interfaces, reasoning over complex content, and emitting precise actions (pyautogui-style computer use) or valid JSON function calls.

At a 1.7GB footprint, Supertron3-0.8B delivers competitive agentic performance for low-latency, on-device deployment.

Developed by: Suprem Org
Model type: Vision-Language Model for Navigation, Computer Use, and Tool Calling Agents
Architecture: Hybrid Gated DeltaNet + Attention (24 layers, 1024 hidden), 0.8B params, Vision Encoder, 262K native context
Fine-tuned from model: Qwen/Qwen3.5-0.8B
License: Apache 2.0

Get Started

Transformers

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

model = AutoModelForImageTextToText.from_pretrained(
    "Surpem/Supertron3-0.8B", trust_remote_code=True,
    torch_dtype=torch.bfloat16, device_map="auto"
)
processor = AutoProcessor.from_pretrained("Surpem/Supertron3-0.8B", trust_remote_code=True)

messages = [
    {"role":"system","content": "You are Supertron3, precise tool caller. Output ONLY JSON array of tool calls.\nAvailable tools:\n[{\"name\":\"get_weather\",\"description\":\"get weather\",\"parameters\":{\"properties\":{\"city\":{\"type\":\"string\"}}}}]"},
    {"role":"user","content": "What's weather in Paris on 2026-09-15?"}
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(processor.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

vLLM / SGLang

vllm serve Surpem/Supertron3-0.8B --dtype bfloat16 --max-model-len 8192

Results

Tool Calling & Agentic Benchmarks

Supertron3-0.8B was evaluated on BFCL-style function calling (single-call, multi-tool, nested arguments) alongside real-world web-agent and computer-use benchmarks (Mind2Web, OmniAct). Despite being the smallest model in the comparison, Supertron3 ranks first on BFCL while being the only model that can reliably act on a desktop — the base models score higher on generic tool priors but fail completely at computer use.

Table 1: Evaluation results on tool calling, web navigation, and computer-use benchmarks.

Model	Params	BFCL ↑	Mind2Web (step acc) ↑	Computer Use ↑
Supertron3-0.8B (ours)	0.8B	82%	77%	100%
Qwen3.5-0.8B (base)	0.8B	56%	80%	0%
North Micro Vision Instruct	~2B	61%	—	—
Qwen3.5-4B	4B	69%	—	—

Computer Use & Grounding

Supertron3 excels at localizing UI elements and emitting executable actions — a capability entirely absent in the base model. The finetune taught the base to act, not just chat.

Limitations

0.8B capacity: strong single-turn tool routing and short-horizon computer use; long-horizon workflows remain open.
Grounding ceiling: ScreenSpot-Pro-class precision requires larger vision encoders.

Citation

@misc{suprem2026supertron3,
      title={Supertron3-0.8B: Edge Foundation Model for Tool Calling and Computer Use Agents},
      author={Suprem},
      year={2026},
      url={https://huggingface.co/Surpem/Supertron3-0.8B},
}
@article{qwen35,
  title={Qwen3.5 Technical Report},
  year={2026}
}