Instructions to use AksaraLLM/Kiel-Pro-0.5B-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AksaraLLM/Kiel-Pro-0.5B-v3 with Transformers:

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

pipe = pipeline("text-generation", model="AksaraLLM/Kiel-Pro-0.5B-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AksaraLLM/Kiel-Pro-0.5B-v3")
model = AutoModelForCausalLM.from_pretrained("AksaraLLM/Kiel-Pro-0.5B-v3")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AksaraLLM/Kiel-Pro-0.5B-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AksaraLLM/Kiel-Pro-0.5B-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AksaraLLM/Kiel-Pro-0.5B-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AksaraLLM/Kiel-Pro-0.5B-v3

SGLang

How to use AksaraLLM/Kiel-Pro-0.5B-v3 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 "AksaraLLM/Kiel-Pro-0.5B-v3" \
    --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": "AksaraLLM/Kiel-Pro-0.5B-v3",
		"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 "AksaraLLM/Kiel-Pro-0.5B-v3" \
        --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": "AksaraLLM/Kiel-Pro-0.5B-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AksaraLLM/Kiel-Pro-0.5B-v3 with Docker Model Runner:
```
docker model run hf.co/AksaraLLM/Kiel-Pro-0.5B-v3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Kiel-Pro-0.5B-v3

A 494M-parameter Indonesian language model based on the Qwen2 architecture, continued-pretrained / fine-tuned for Indonesian by the AksaraLLM community. This is the smallest fully-working AksaraLLM model: it loads cleanly via AutoModelForCausalLM, includes its own tokenizer, and produces coherent Indonesian text on standard prompts.

Measured baseline (Devin audit, CPU bf16, 50 short Indonesian sentences)

Metric	Value
Perplexity	14.7
English-stopword ratio in ID-prompted output	0.8%
Indonesian-stopword ratio in ID-prompted output	23.2%
Parameters	494.0 M
Architecture	Qwen2ForCausalLM

Sample generations

Indonesia adalah negara → coherent, factual Indonesian completion.
Resep nasi goreng yang enak adalah → coherent recipe-style Indonesian.

Quickstart

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("AksaraLLM/Kiel-Pro-0.5B-v3")
model = AutoModelForCausalLM.from_pretrained(
    "AksaraLLM/Kiel-Pro-0.5B-v3",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
inp = tok("Indonesia adalah negara", return_tensors="pt").to(model.device)
print(tok.decode(model.generate(**inp, max_new_tokens=100, do_sample=True, top_p=0.9)[0], skip_special_tokens=True))

Limitations

No chat template in the tokenizer config — treat this as a base LM, not an instruction-tuned model.
Identity is uncalibrated — the model is descended from Qwen2 and may identify itself as "Qwen" if asked. A short identity SFT pass would fix this.
Hallucinations typical of a 0.5B-parameter model: factual claims should always be verified.
No production guardrails / RLHF.

Recommended downstream work

Identity SFT (50–100 prompts) to teach the model to say "AksaraLLM Kiel-Pro".
Add a chat_template (Qwen2 ChatML) to tokenizer_config.json.
Run IndoNLU / IndoMMLU benchmarks for a publishable score.

License

Apache 2.0

Sample outputs (CPU inference, greedy, repeat_penalty=1.15)

Real generation captured during the Devin audit on 2026-05-02. Greedy decoding (do_sample=False) so these are reproducible.

Continuations (no chat template)

Prompt: Indonesia adalah negara kepulauan terbesar di dunia,
Continuation: dan memiliki 1.347 pulau kecil yang disebut indah rindu (dari bahasa Jawa: rumput). Indra ini berjumlah sekitar 200 pulau kecil, termasuk Pulau Madura, Pulau Sunda, Pulau Maluku, Pulau Nusa Tenggara, Pulau Flores,

Prompt: Resep sederhana untuk membuat nasi goreng adalah
Continuation: : A. 200 gram nasi, 150 ml air, 3 lembar daun jeruk nipis, dan 4 bawang putih. B. 200 gram nasi, 150 ml air, 3 lembar daun jeruk nipis, dan 6 bawang putih. C. 2

Prompt: Ibukota Republik Indonesia adalah
Continuation: ibu kota negara dan pusat pemerintahan dari Indonesia. Ibukotanya terletak di Sumatera Barat, Indonesia. Penduduknya berjumlah 2.354.069 jiwa (2010). Ibukota dipimpin oleh Kepala Pemerintah Indonesia yang sekaligus menjadi Presiden Rep

Prompt: Bahasa Indonesia berasal dari rumpun bahasa
Continuation: Indo-Eropa klasik yang dinamai Sinhalese. Dari sisi etimologi, kata "sinah" berarti "pohon", sedangkan "lese" berarti "menggambar". Kata ini muncul dalam bahasa Sinhala dan merupakan salah satu nama untuk pohon-pohon di daerah itu. Pohon tersebut adalah pohon le

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Safetensors

Model size

0.5B params

Tensor type

F16

Model tree for AksaraLLM/Kiel-Pro-0.5B-v3

Base model

Qwen/Qwen2-0.5B

Finetuned

(140)

this model

Finetunes

1 model

Quantizations

2 models