Instructions to use onalennainnocent/mzansilm-125m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use onalennainnocent/mzansilm-125m with Transformers:

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

pipe = pipeline("text-generation", model="onalennainnocent/mzansilm-125m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use onalennainnocent/mzansilm-125m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "onalennainnocent/mzansilm-125m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onalennainnocent/mzansilm-125m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/onalennainnocent/mzansilm-125m

SGLang

How to use onalennainnocent/mzansilm-125m 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 "onalennainnocent/mzansilm-125m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onalennainnocent/mzansilm-125m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "onalennainnocent/mzansilm-125m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onalennainnocent/mzansilm-125m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use onalennainnocent/mzansilm-125m with Docker Model Runner:
```
docker model run hf.co/onalennainnocent/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

Downloads last month: 24

Safetensors

Model size

0.1B params

Tensor type

F32

Paper for onalennainnocent/mzansilm-125m

MzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages

Paper • 2603.20732 • Published Mar 21