Instructions to use Surpem/Supertron2-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Surpem/Supertron2-24B with Transformers:

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

pipe = pipeline("text-generation", model="Surpem/Supertron2-24B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Surpem/Supertron2-24B")
model = AutoModelForImageTextToText.from_pretrained("Surpem/Supertron2-24B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Surpem/Supertron2-24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Surpem/Supertron2-24B"
# 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/Supertron2-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Surpem/Supertron2-24B

SGLang

How to use Surpem/Supertron2-24B 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/Supertron2-24B" \
    --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/Supertron2-24B",
		"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/Supertron2-24B" \
        --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/Supertron2-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Surpem/Supertron2-24B with Docker Model Runner:
```
docker model run hf.co/Surpem/Supertron2-24B
```

Supertron2-24B: A Capable Instruction-Tuned Coding and Reasoning Model

Model Description

Supertron2-24B is an instruction-tuned language model built on top of mistralai/Devstral-Small-2-24B-Instruct-2512. It is designed for practical coding assistance, structured reasoning, math, science, general chat, and everyday instruction following.

Developed by: Surpem
Model type: Causal Language Model
Architecture: Dense Transformer, 24B parameters
License: Apache 2.0

Capabilities

Coding

Supertron2-24B is designed to help write, explain, and debug code. It can assist with practical programming tasks, implementation planning, error analysis, and code review style explanations.

Reasoning

The model can work through multi-step questions, compare options, follow structured instructions, and produce concise answers when requested.

Math

Supertron2-24B can handle arithmetic, algebra-style problems, word problems, and step-by-step mathematical explanations.

Science

The model can explain scientific concepts clearly, answer STEM questions, and help with educational or technical writing.

General Chat

Supertron2-24B can assist with writing, brainstorming, explanations, planning, summarization, and general everyday questions.

Get Started

from transformers import AutoTokenizer, AutoModelForImageTextToText
import torch

model_id = "Surpem/Supertron2-24B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "user", "content": "Write a Python function that checks if a string is a palindrome."}
]

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	48 GB	80 GB+
4-bit quantized	16 GB	24 GB+

For long contexts or larger batches, use more VRAM or reduce batch size and max sequence length.

Intended Use

Supertron2-24B is intended for:

Coding assistance
Software engineering reasoning
Math and science explanations
General chat and instruction following
Writing, summarization, and brainstorming
Research and technical assistance

Limitations

The model can make mistakes and should be checked for important work.
It may produce incorrect code, incomplete reasoning, or outdated information.
It should not be used as the only source for medical, legal, financial, or safety-critical decisions.
Generated code should be reviewed and tested before use.

Citation

@misc{surpem2026supertron2-24b,
      title={Supertron2-24B -- Instruction-Tuned Coding and Reasoning Model},
      author={Surpem},
      year={2026},
      url={https://huggingface.co/Surpem/Supertron2-24B},
}