Supertron1-4B

Supertron1-4B: A Capable, Efficient Instruction-Tuned Language Model

Model Description

Supertron1-4B is an instruction-tuned language model built on top of Qwen3-4B. Designed to be a reliable, efficient daily driver, it delivers strong performance across math, coding, reasoning, and general conversation while remaining fast and lightweight enough to run on consumer hardware.

• Developed by: Surpem
• Model type: Causal Language Model
• Architecture: Dense Transformer, 4B parameters
• Fine-tuned from: Qwen/Qwen3-4B
• License: Apache 2.0

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Results

Supertron1-4B holds its own against models in the 4–8B class and surpasses Mistral 7B on all four core benchmarks despite having nearly half the parameters.

<div align="center"> <p align="center"><a href="https://postimg.cc/zLfkBN3D"><img width=800 src="https://i.postimg.cc/0NYXV2sS/1000037258.png"/></a></p> </div>

Key takeaways:

• Beats Mistral 7B on every benchmark at 4B parameters
• Strong GSM8K and HumanEval performance from math and coding focused tuning
• Competitive with Phi-4 mini on a fraction of the compute

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Get Started

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "surpem/supertron1-4b"

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))

Citation

@misc{surpem2026supertron1,
      title={Supertron1-4B — Efficient Instruction-Tuned Language Model},
      author={Surpem},
      year={2026},
      url={https://huggingface.co/surpem/supertron1-4b},
}