---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-1.7B
pipeline_tag: text-generation
library_name: transformers
tags:
- reasoning
- math
- coding
- instruction-tuned
- pytorch
---
# **Supertron2-1.7B: A Compact, Efficient Instruction-Tuned Language Model**
## **Model Description**
**Supertron2-1.7B** is an instruction-tuned language model built on top of Qwen3-1.7B. Designed to be a **reliable, efficient daily driver**, it delivers strong performance across math, coding, reasoning, science, general knowledge, and general conversation while remaining lightweight enough to run on consumer hardware.

* **Developed by:** Surpem
* **Model type:** Causal Language Model
* **Architecture:** Dense Transformer, 1.7B parameters
* **Fine-tuned from:** [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
* **License:** Apache 2.0

---

## **Capabilities**

### **Reasoning**
Supertron2-1.7B is designed for clear multi-step reasoning, making it capable of breaking down complex problems in a structured and useful way. It can work through questions methodically rather than jumping directly to a final answer.

### **Math**
The model handles a range of math tasks, from arithmetic and algebra to word problems and structured problem solving. It is useful for explaining steps, checking calculations, and producing concise final answers.

### **Coding**
Supertron2-1.7B can write, debug, and explain code across popular languages including Python, JavaScript, C++, and more. It understands syntax, common programming patterns, algorithmic reasoning, and practical implementation details.

### **Science & General Knowledge**
Broad instruction tuning across science, STEM, and general knowledge domains means the model can hold technical conversations, explain difficult concepts clearly, and assist with research, writing, and analysis tasks.

### **Instruction Following**
The model is responsive to natural language instructions. Whether you need concise answers, detailed explanations, structured output, or creative writing, Supertron2-1.7B adapts to the format and tone you ask for without needing complex prompting tricks.

---

## **Get Started**
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Surpem/Supertron2-1.7B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Explain the difference between LoRA and full fine-tuning."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
```

---

## **Hardware Requirements**
| Precision | Min VRAM | Recommended |
|---|---|---|
| bfloat16 | 5 GB | 8 GB+ |
| 4-bit quantized | 3 GB | 4 GB+ |

For 4-bit quantized inference:
```python
from transformers import BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto")
```

---

## **Citation**
```bibtex
@misc{surpem2026supertron2-1.7b,
      title={Supertron2-1.7B — Efficient Instruction-Tuned Language Model},
      author={Surpem},
      year={2026},
      url={https://huggingface.co/Surpem/Supertron2-1.7B},
}
```