---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3.5-0.8B
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- multimodal
- action
- agent
- pytorch
- computer use
- gui agents
- tool-calling
- edge
---

# **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**](https://surpem.qzz.io)
* **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

```bash
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}
}
```