Instructions to use Surpem/Supertron1-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Surpem/Supertron1-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Surpem/Supertron1-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Surpem/Supertron1-4B")
model = AutoModelForCausalLM.from_pretrained("Surpem/Supertron1-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Surpem/Supertron1-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Surpem/Supertron1-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Surpem/Supertron1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Surpem/Supertron1-4B

SGLang

How to use Surpem/Supertron1-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Surpem/Supertron1-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Surpem/Supertron1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Surpem/Supertron1-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Surpem/Supertron1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Surpem/Supertron1-4B with Docker Model Runner:
```
docker model run hf.co/Surpem/Supertron1-4B
```

Supertron1-4B / README.md

GideonHF

Update README.md

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	---
	license: apache-2.0
	language:
	- en
	base_model:
	- Qwen/Qwen3-4B
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- reasoning
	- math
	- coding
	- instruction-tuned
	- pytorch
	---

	# 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](https://huggingface.co/Qwen/Qwen3-4B)
	* License: Apache 2.0

	---

	## 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

	---

	## Get Started

	```python
	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

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