Instructions to use AksaraLLM/aksarallm-1.5b-native with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AksaraLLM/aksarallm-1.5b-native with Transformers:

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

pipe = pipeline("text-generation", model="AksaraLLM/aksarallm-1.5b-native")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AksaraLLM/aksarallm-1.5b-native")
model = AutoModelForCausalLM.from_pretrained("AksaraLLM/aksarallm-1.5b-native")
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/aksarallm-1.5b-native with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AksaraLLM/aksarallm-1.5b-native"
# 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/aksarallm-1.5b-native",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AksaraLLM/aksarallm-1.5b-native

SGLang

How to use AksaraLLM/aksarallm-1.5b-native 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/aksarallm-1.5b-native" \
    --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/aksarallm-1.5b-native",
		"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/aksarallm-1.5b-native" \
        --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/aksarallm-1.5b-native",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AksaraLLM/aksarallm-1.5b-native with Docker Model Runner:
```
docker model run hf.co/AksaraLLM/aksarallm-1.5b-native
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

aksarallm-1.5b-native

The first fully from-scratch AksaraLLM 1.5B model (2.04B actual params), LLaMA-style architecture. Where the AksaraLLM-Qwen-1.5B* line is descended from Qwen2, this checkpoint contains no inherited weights — it was trained from random init on AksaraLLM's own corpus and tokenizer.

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

Metric	Value
Perplexity	113.5 (much higher than Qwen-derived models, see below)
English-stopword ratio in ID-prompted output	0.0%
Indonesian-stopword ratio in ID-prompted output	31.3% (highest of any AksaraLLM model — most Indonesian-saturated)
Parameters	2039.0 M
Architecture	LlamaForCausalLM
Vocabulary	151 665

Why the high perplexity?

This model started from random init and has been trained on a smaller corpus than the Qwen2-derived models, which began with ~5 T tokens of pretraining already baked in. PPL ≈ 113 reflects "model is converging on Indonesian distribution but not fully there yet". The very high Indonesian-word ratio (31%) and zero English leak suggest the model is producing Indonesian-only output even when uncertain — a useful signal that the language identity is correctly trained, but the lexical / factual quality is below the Qwen-derived models.

This is the honest from-scratch baseline for the AksaraLLM project. It is the right reference point when measuring how much value continued pretraining / from-scratch with a larger corpus delivers (which is exactly what the planned 20B aims to demonstrate).

Loading notes

The checkpoint contains legacy rope.sin_cached and rope.cos_cached keys that are unexpected by HF's LlamaForCausalLM; HF silently drops them on load — this is benign. Same tie_word_embeddings config / checkpoint mismatch as the Qwen variants; recommend setting tie_word_embeddings: false in config.json.

Quickstart

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("AksaraLLM/aksarallm-1.5b-native")
model = AutoModelForCausalLM.from_pretrained(
    "AksaraLLM/aksarallm-1.5b-native",
    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=120, do_sample=True, top_p=0.9)[0], skip_special_tokens=True))

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 berada di kawasan Indonesia. Kebantahannya juga mengalami perubahan kecil yang memang tidak menentu. Pada tahun 1930, seorang pemerintat diperlakukan dengan pengelolaan yang lebih baik. Dalam permainan ini, perlu berbentuk kota dan kembali ke khus

Prompt: Resep sederhana untuk membuat nasi goreng adalah
Continuation: kandungan yang tidak akan bermanfaat dengan baik. Kota ini berikut dihadirkan pada kecilnya jelajah kembali dan menghasilkan khusus yang terbaik, dan memperhatikan perawatan dan pemeras kesehatan. Kembang kota juga menawarkan kota yang lebih banyak dipermainkan oleh

Prompt: Ibukota Republik Indonesia adalah
Continuation: kembali menggantikan pemerintahan Kepala Paling Jaya di Indonesia. Dalam Kualifikasi Tengku Sosial (KAN) 1029, Bapak M. Kusyari Pada tahun 1354 Kepala KAN berbagai permainan kecil yang terjadi dengan memperbaiki k

Prompt: Bahasa Indonesia berasal dari rumpun bahasa
Continuation: Kakik, dan permainan ini berbeda-beda. Dalam pemerintahan Jepang, kota Pekanba adalah kekuatan yang terlalu banyak di seorang penghiper 1930-2050. Tidak tidak mengbihnya untuk memperhatikan kembali kekayaan dan kegagalan ke

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