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language:
- id
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- indonesian
- aksarallm
- archived
- research
---
# Kiel-26M-Matured
> ⚠️ **Status: research artifact / early experiment, not a usable language model today.**
> The tokenizer used at training time was not preserved in this repository,
> so the checkpoint cannot be loaded end-to-end with HF `AutoModel` /
> `AutoTokenizer`. Output quality on standard Indonesian benchmarks is far
> below the org's working models (`Kiel-Pro-0.5B-v3`, `AksaraLLM-Qwen-1.5B-v5-public`).
## What this is
A **39M-parameter** decoder-only transformer trained from scratch as
part of the early AksaraLLM experiments. Architecture (inferred from weight
shapes):
| Property | Value |
|----------|-------|
| Parameters | 38.7M |
| Layers | 6 |
| Heads | 6 |
| Hidden size | 384 |
| FFN size (SwiGLU) | 1536 |
| Vocabulary | 32000 |
| Context length | 256 |
| RMSNorm + RoPE + SwiGLU | yes |
## Measured baseline (Devin audit, CPU eval)
We loaded this checkpoint with `aksarallm.model.aksaraLLMModel`, tested several
candidate tokenizers (`AksaraLLM/aksara-tokenizer-v1/v2/v3`, Llama-2 SentencePiece,
GPT-2 BPE), and ran perplexity on 50 short Indonesian Wikipedia-style sentences
plus 5 free-form prompts. Best-case results:
- **Perplexity**: BROKEN (tokenizer mismatch — see Limitations) — meaning the model is **not** modelling Indonesian distribution.
- **English-stopword ratio in ID-prompted output**: 0.0%
- **Indonesian-stopword ratio in ID-prompted output**: 0.0%
Sample output for prompt "Indonesia adalah negara":
```
Indonesia adalah negara2 March enough pur streCon compens4!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
```
## Why the original "Skor 10/11 (Grade S)" claim is misleading
The score in earlier versions of this README is from a custom 11-question
in-house scorecard graded on a tiny SFT set, not from a standard language
modelling metric. By any standard LM evaluation (perplexity, response
coherence on out-of-distribution prompts, identity calibration), this model
does not function.
## Limitations
- **Tokenizer not preserved.** Without it, downstream usage is impossible.
- **No HF-compatible config.json** in the original training pipeline; the
` aksarallm` package is required for loading.
- **Vocab size 32000** — does not match any published AksaraLLM
tokenizer (32 768, 64 000) or common open tokenizers (Llama-2 = 32 000).
- **Trained on a small mixed corpus** (Wikipedia / SFT pairs), insufficient
for general Indonesian generation at this scale.
## What to use instead
If you want a small Indonesian LM in the AksaraLLM family, use:
- [`AksaraLLM/Kiel-Pro-0.5B-v3`](https://huggingface.co/AksaraLLM/Kiel-Pro-0.5B-v3) — 494M Qwen2-based, perplexity ≈ 15 on the same eval set.
- [`AksaraLLM/AksaraLLM-Qwen-1.5B-v5-public`](https://huggingface.co/AksaraLLM/AksaraLLM-Qwen-1.5B-v5-public) — 1.78B Qwen2-based, perplexity ≈ 8.4.
## Citation
```
@misc{aksarallm-kiel-26m-matured,
author = {Cahyok Putra and AksaraLLM Community},
title = {Kiel-26M-Matured: early-experiment Indonesian transformer (39M params)},
year = 2025,
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/AksaraLLM/Kiel-26M-Matured}},
}
```
## License
Apache 2.0
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