anrilombard / mzansilm-125m

MzansiLM 125M

MzansiLM is a 125M-parameter decoder-only language model trained from scratch on MzansiText , a multilingual corpus covering all eleven official South African languages.

Model Details

• Parameters: 125,008,384
• Architecture: decoder-only LlamaForCausalLM
• Hidden size: 512
• Intermediate size: 1536
• Layers: 30
• Attention heads: 9
• Key/value heads: 3
• Context length: 2048
• RoPE theta: 10000.0
• RMSNorm epsilon: 1e-5
• Tied word embeddings: true
• Training attention implementation: flash_attention_2

Tokenizer

MzansiLM uses a custom BPE tokenizer with a vocabulary size of 65536 .

• [BOS] = 0
• [EOS] = 1
• [PAD] = 2
• [UNK] = 3
• Normalizer: NFD
• Pre-tokenizer: ByteLevel
• Post-processing:
  • single sequence: [BOS] $A [EOS]
  • pair sequence: [BOS] $A [EOS] [BOS] $B [EOS]

Training Data

The model was trained on MzansiText and covers all eleven official South African languages:

af , en , nso , sot , ssw , tsn , tso , ven , xho , zul , nbl

Related releases:

• Raw corpus: anrilombard/mzansi-text
• Tokenized corpus: anrilombard/mzansi-text-tokenized
• GitHub code and configs: https://github.com/Anri-Lombard/sallm

Intended Use

MzansiLM is a research model for pretraining, fine-tuning, and evaluation on South African languages. It is intended as a reproducible baseline for language modeling and downstream task adaptation.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("anrilombard/mzansilm-125m")
model = AutoModelForCausalLM.from_pretrained("anrilombard/mzansilm-125m")

inputs = tokenizer("Molo!", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation

Until the public paper record is live, please cite the repository:

@misc{mzansitext-mzansilm-github,
  title = {MzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages},
  author = {Lombard, Anri and Mawere, Simbarashe and Aina, Temi and Wolff, Ethan and Gumede, Sbonelo and Novick, Elan and Meyer, Francois and Buys, Jan},
  year = {2026},
  howpublished = {\url{https://github.com/Anri-Lombard/sallm}},
  note = {GitHub repository}
}

License

Apache License 2.0