Instructions to use Gokturk97/AstrumN1Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gokturk97/AstrumN1Mini with Transformers:

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

pipe = pipeline("text-generation", model="Gokturk97/AstrumN1Mini")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Gokturk97/AstrumN1Mini")
model = AutoModelForCausalLM.from_pretrained("Gokturk97/AstrumN1Mini", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Gokturk97/AstrumN1Mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gokturk97/AstrumN1Mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gokturk97/AstrumN1Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Gokturk97/AstrumN1Mini

SGLang

How to use Gokturk97/AstrumN1Mini 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 "Gokturk97/AstrumN1Mini" \
    --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": "Gokturk97/AstrumN1Mini",
		"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 "Gokturk97/AstrumN1Mini" \
        --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": "Gokturk97/AstrumN1Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Gokturk97/AstrumN1Mini with Docker Model Runner:
```
docker model run hf.co/Gokturk97/AstrumN1Mini
```

AstrumN1Mini

AstrumN1Mini is a 0.5B-parameter instruction-tuned language model with a focus on Turkish and English. It is the first model of the Astrum family (formerly Alary) by Göktürk Bağbaşı, built on top of Qwen/Qwen2.5-0.5B-Instruct.

It is a small, fast model meant for experimentation, lightweight local use, and as a baseline for the next models in the N-series.

Model details


Parameters	0.5B
Base model	Qwen/Qwen2.5-0.5B-Instruct
Languages	Turkish, English
Context length	32,768 tokens
License	Apache-2.0

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Gokturk97/AstrumN1Mini"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

messages = [{"role": "user", "content": "Merhaba! Kendini kısaca tanıtır mısın?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

output = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Training

Data: 10M tokens of Turkish instruction data
Hardware: Kaggle, 2x NVIDIA T4

Limitations

This is a very small model, so please keep expectations realistic:

Factual knowledge is unreliable, especially geography (for example, country capitals can be wrong).
Arithmetic and multi-step reasoning are weak.
It can hallucinate confidently. Do not use it for medical, legal, financial, or other high-stakes decisions.
Turkish and English are the target languages; other languages are not a focus.

Roadmap

Next in the series: N1.1 mini (successor to this model), N2 mini (2B), and larger Astrum models.

Türkçe özet

AstrumN1Mini, Türkçe ve İngilizce odaklı, 0.5B parametreli küçük bir dil modelidir. Qwen2.5-0.5B-Instruct tabanlıdır ve Apache-2.0 lisansıyla açık olarak paylaşılmıştır. Eğitimde 10M token Türkçe instruction verisi kullanılmıştır. Küçük bir model olduğu için özellikle coğrafya ve matematik gibi konularda hata yapabilir; deneme ve hafif kullanım amaçlıdır.

Contact

Feedback and issues are welcome in the community tab of this repo.

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Safetensors

Model size

0.5B params

Tensor type

F16

Model tree for Gokturk97/AstrumN1Mini

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct