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:

• Paper: arXiv:2603.20732
• 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

Please cite the paper:

@misc{lombard2026mzansitextmzansilmopencorpus,
      title={MzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages}, 
      author={Anri Lombard and Simbarashe Mawere and Temi Aina and Ethan Wolff and Sbonelo Gumede and Elan Novick and Francois Meyer and Jan Buys},
      year={2026},
      eprint={2603.20732},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.20732}, 
}

License

Apache License 2.0