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
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},
}