Supertron1-8B: A Capable, Efficient Instruction-Tuned Language Model

Model Description

Supertron1-8B is an instruction-tuned language model built on top of Qwen3-8B-Base. Designed to be a reliable, efficient daily driver, it delivers strong performance across math, coding, reasoning, and general conversation while remaining fast enough to run on consumer hardware with a capable GPU.

• Developed by: Surpem
• Model type: Causal Language Model
• Architecture: Dense Transformer, 8B parameters
• Fine-tuned from: Qwen/Qwen3-8B-Base
• Fine-tuning method: LoRA (r=16, alpha=32, all-linear targets)
• License: Apache 2.0

---

Capabilities

Reasoning

Supertron1-8B was trained on long-form chain-of-thought reasoning traces, making it capable of breaking down complex multi-step problems clearly and methodically. It thinks through problems before answering rather than jumping to conclusions, resulting in more reliable and explainable outputs.

Math

With dedicated training on competition-style math problems and step-by-step solutions, the model handles everything from algebra and calculus to word problems with structured, verifiable working. It consistently shows its reasoning rather than just producing a final answer.

Coding

Supertron1-8B can write, debug, and explain code across popular languages including Python, JavaScript, C++, and more. Trained on filtered, high-quality coding instruction data, it understands not just syntax but software design patterns, algorithmic thinking, and best practices.

Science & General Knowledge

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

Instruction Following

The model is highly responsive to natural language instructions. Whether you need concise answers, detailed explanations, structured output, or creative writing, Supertron1-8B adapts to the format and tone you ask for without needing complex prompting tricks.

---

Get Started

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "surpem/supertron1-8b"

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

For 4-bit quantized inference:

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

@misc{surpem2026supertron1-8b,
      title={Supertron1-8B — Efficient Instruction-Tuned Language Model},
      author={Surpem},
      year={2026},
      url={https://huggingface.co/surpem/supertron1-8b},
}