初始化项目，由ModelHub XC社区提供模型

Model: anrilombard/mzansilm-125m
Source: Original Platform

README.md

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+---
+language:
+- af
+- en
+- nso
+- sot
+- ssw
+- tsn
+- tso
+- ven
+- xho
+- zul
+- nbl
+tags:
+- llama
+- south-african-languages
+- low-resource
+- decoder-only
+- mzansilm
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# 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.
+
+[![GitHub](https://img.shields.io/badge/GitHub-Anri--Lombard/sallm-blue)](https://github.com/Anri-Lombard/sallm)
+[![Paper](https://img.shields.io/badge/Paper-arXiv_2603.20732-red.svg)](https://arxiv.org/abs/2603.20732)
+[![Dataset](https://img.shields.io/badge/Dataset-MzansiText-green)](https://huggingface.co/datasets/anrilombard/mzansi-text)
+[![Collection](https://img.shields.io/badge/Collection-MzansiLM-orange)](https://huggingface.co/collections/anrilombard/mzansilm-69635ca7b60efedb9dfcb09e)
+
+## 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](https://arxiv.org/abs/2603.20732)
+- Raw corpus: [anrilombard/mzansi-text](https://huggingface.co/datasets/anrilombard/mzansi-text)
+- Tokenized corpus: [anrilombard/mzansi-text-tokenized](https://huggingface.co/datasets/anrilombard/mzansi-text-tokenized)
+- GitHub code and configs: [https://github.com/Anri-Lombard/sallm](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
+
+```python
+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:
+
+```bibtex
+@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