Instructions to use AksaraLLM/AksaraLLM-20B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AksaraLLM/AksaraLLM-20B with Transformers:

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

pipe = pipeline("text-generation", model="AksaraLLM/AksaraLLM-20B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AksaraLLM/AksaraLLM-20B")
model = AutoModelForCausalLM.from_pretrained("AksaraLLM/AksaraLLM-20B")
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-20B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AksaraLLM/AksaraLLM-20B"
# 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-20B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AksaraLLM/AksaraLLM-20B

SGLang

How to use AksaraLLM/AksaraLLM-20B 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-20B" \
    --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-20B",
		"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-20B" \
        --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-20B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AksaraLLM/AksaraLLM-20B with Docker Model Runner:
```
docker model run hf.co/AksaraLLM/AksaraLLM-20B
```

AksaraLLM 20B (dense)

Status: architecture + tokenizer published. Weights are NOT YET trained. This repository currently holds the architecture config and tokenizer. The from-scratch pretraining run is blocked on TRC v5p-128 approval; see Roadmap below.

AksaraLLM 20B is a from-scratch, Indonesian-first decoder-only transformer designed to serve Indonesian (id), Malay (ms), Javanese (jv), Sundanese (su), with English (en) and source code as secondary.

Architecture

Field	Value
Family	LLaMA-3-style decoder-only transformer
Parameters	20,359,673,856 (20.36 B, with tied embeddings)
Hidden size	6,144
FFN inner	20,480 (SwiGLU)
Layers	42
Attention heads	48 query / 8 KV (GQA, 6:1)
Head dim	128
Vocab	131,072 (BPE byte-level)
Positional	RoPE, θ = 1,000,000
Context (pretrain)	8,192
Context (YaRN extend)	32,768
Context (inference target)	131,072
Norm	RMSNorm
Embeddings	tied

Tokenizer

The tokenizer is already published at Ezekiel999/aksara-tokenizer-20b and mirrored here.

Fertility (held-out samples):

Language	Source	tokens/word	Target
English	FineWeb	1.280	≤ 1.40
Indonesian	Wikipedia	1.357	≤ 1.60
Indonesian	CulturaX web	1.215	≤ 1.60
Malay	Wikipedia	1.368	≤ 1.60
Javanese	Wikipedia	1.657	≤ 1.80

Roadmap

Phase	Status	Compute	Target date
1. Architecture + tokenizer	✅ Done	CPU	2026-04
2. Corpus build (400–600B tokens)	🔄 in progress	v6e-8	2026-05
3. Pretrain phase 1 (8k context, 400B tokens)	⏸ blocked on TRC v5p-128	v5p-128, 4–5 weeks	2026-06
4. YaRN context extension (32k)	pending	v5p-128, ~4 days	2026-07
5. SFT	pending	v5p-64 or v6e-8	2026-07
6. DPO / ORPO	pending	v5p-64 or v6e-8	2026-07
7. Eval + release (GGUF)	pending	CPU	2026-08

Usage (tokenizer only)

from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained("Ezekiel999/aksara-tokenizer-20b")
print(tok("Halo AksaraLLM", add_special_tokens=False).input_ids)

Weights will be published here once pretraining completes.

Citation

@misc{aksarallm2026,
  title = {AksaraLLM 20B: A From-Scratch Indonesian-First Language Model},
  author = {AksaraLLM Team},
  year = {2026},
  url = {https://huggingface.co/AksaraLLM/AksaraLLM-20B}
}

License

Apache-2.0. Pre-training data attribution will be documented with the final weights.

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