Supertron1-8B/README.md

---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
tags:
- reasoning
- math
- coding
- instruction-tuned
- pytorch
---
# **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](https://huggingface.co/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**
```python
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**
| Precision | Min VRAM | Recommended |
|---|---|---|
| bfloat16 | 18 GB | 24 GB (RTX 3090/4090) |
| 4-bit quantized | 8 GB | 12 GB (RTX 3060/4070) |

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