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

python

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

bibtex

@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}
}