初始化项目，由ModelHub XC社区提供模型

Model: McGill-NLP/AfriqueGemma-12B Source: Original Platform
2026-04-14 01:39:04 +08:00
commit 1b9587f911
19 changed files with 52877 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,199 @@
+---
+library_name: transformers
+license: cc-by-4.0
+base_model: google/gemma-3-12b-pt
+language:
+  - af  # Afrikaans
+  - am  # Amharic
+  - ar  # Arabic
+  - en  # English
+  - fr  # French
+  - ha  # Hausa
+  - ig  # Igbo
+  - mg  # Malagasy (Plateau)
+  - ny  # Nyanja
+  - om  # Oromo
+  - pt  # Portuguese
+  - rw  # Kinyarwanda
+  - sn  # Shona
+  - so  # Somali
+  - st  # Southern Sotho
+  - sw  # Swahili
+  - ti  # Tigrinya
+  - tn  # Tswana
+  - xh  # Xhosa
+  - yo  # Yoruba
+  - zu  # Zulu
+pipeline_tag: text-generation
+tags:
+  - african-languages
+  - multilingual
+  - continued-pretraining
+  - afrique-llm
+  - gemma
+  - llamafactory
+---
+
+# AfriqueGemma-12B
+
+## Model Overview
+
+**AfriqueGemma-12B** is part of the **AfriqueLLM** suite—a collection of open language models adapted to **20 African languages** through continued pre-training (CPT) on **25.2B tokens**. This model is based on [google/gemma-3-12b-pt](https://huggingface.co/google/gemma-3-12b-pt) and has been specifically adapted for improved performance on African languages while maintaining strong capabilities in high-resource languages.
+
+### Key Features
+
+- **Type**: Causal Language Model (Base/Pre-trained)
+- **Base Model**: Gemma 3 12B PT
+- **Parameters**: 12B
+- **Context Length**: 8,192 tokens (native)
+- **Training Tokens**: 25.2B tokens of carefully curated multilingual data
+
+## Supported Languages
+
+AfriqueGemma-12B has been adapted for the following 20 African languages plus 4 high-resource languages:
+
+| Language | Code | Family | Script |
+|----------|------|--------|--------|
+| Afrikaans | afr_Latn | Germanic | Latin |
+| Swahili | swh_Latn | Bantu | Latin |
+| Moroccan Arabic | ary_Arab | Semitic | Arabic |
+| Somali | som_Latn | Cushitic | Latin |
+| Amharic | amh_Ethi | Semitic | Ethiopic |
+| Egyptian Arabic | arz_Arab | Semitic | Arabic |
+| Hausa | hau_Latn | Chadic | Latin |
+| Kinyarwanda | kin_Latn | Bantu | Latin |
+| Zulu | zul_Latn | Bantu | Latin |
+| Igbo | ibo_Latn | Volta-Niger | Latin |
+| Plateau Malagasy | plt_Latn | Austronesian | Latin |
+| Xhosa | xho_Latn | Bantu | Latin |
+| Shona | sna_Latn | Bantu | Latin |
+| Yoruba | yor_Latn | Volta-Niger | Latin |
+| Nyanja | nya_Latn | Bantu | Latin |
+| Southern Sotho | sot_Latn | Bantu | Latin |
+| Tigrinya | tir_Ethi | Semitic | Ethiopic |
+| Tunisian Arabic | aeb_Arab | Semitic | Arabic |
+| Oromo | gaz_Latn | Cushitic | Latin |
+| Tswana | tsn_Latn | Bantu | Latin |
+
+**High-resource languages (for catastrophic forgetting mitigation):** English, French, Portuguese, Arabic
+
+## Training Data
+
+Our training corpus combines multiple high-quality sources:
+
+- **African Monolingual Data** (~22.8B tokens): FineWeb2, WURA, and MADLAD-400
+- **Code** (~1B tokens): CornStack-Python for reasoning capabilities
+- **Mathematics** (~1B tokens): FineMath-4+ for mathematical understanding
+- **Synthetic Data** (~324M tokens): GPT-4.1 translated domain-specific content across 10 domains
+
+We use **UniMax sampling** to create a balanced distribution, capping high-resource languages at approximately 1B tokens and upsampling lower-resource languages for up to five epochs.
+
+## Quickstart
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "McGill-NLP/AfriqueGemma-12B"
+
+# Load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# Prepare the model input
+prompt = "Bawo ni o ṣe n ṣe?"  # Yoruba: "How are you doing?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# Generate text
+generated_ids = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9
+)
+output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+print(output)
+```
+
+## Deployment
+
+For deployment, you can use `vllm` or `sglang` to create an OpenAI-compatible API endpoint:
+
+**vLLM:**
+```shell
+vllm serve McGill-NLP/AfriqueGemma-12B
+```
+
+**SGLang:**
+```shell
+python -m sglang.launch_server --model-path McGill-NLP/AfriqueGemma-12B
+```
+
+## Training Details
+
+### Hyperparameters
+
+- **Learning Rate**: 5e-5 (with warmup and cosine decay)
+- **Context Length**: 16,384 tokens
+- **Optimizer**: AdamW
+- **Precision**: BF16 mixed precision
+
+### Infrastructure
+
+Training was conducted using the LLaMA-Factory framework on up to 64 NVIDIA H100 GPUs with:
+- DeepSpeed ZeRO-1/ZeRO-2
+- Flash Attention 3
+- Sequence packing
+- Liger Kernel optimizations
+
+## Evaluation
+
+All AfriqueLLM models are evaluated on multiple multilingual benchmarks:
+
+| Model | AfriMGSM | AfriMMLU | AfriXNLI | Belebele | FLORES | INJONG | SIB-200 | Overall | Δ |
+|-------|----------|----------|----------|----------|--------|--------|---------|----------|---|
+| [Gemma3-4B](https://huggingface.co/google/gemma-3-4b-pt) | 10.24 | 33.89 | 37.76 | 45.79 | 29.50 | 55.52 | 63.59 | 39.47 | |
+| [AfriqueGemma-4B](https://huggingface.co/McGill-NLP/AfriqueGemma-4B) | 14.86 | 36.73 | 39.62 | 50.52 | 57.31 | 69.28 | 69.21 | 48.22 | +8.7 (22.2%) |
+| [Gemma3-12B](https://huggingface.co/google/gemma-3-12b-pt) | 25.21 | 48.76 | 44.01 | 68.84 | 40.16 | 73.53 | 79.17 | 54.24 | |
+| <u>[AfriqueGemma-12B](https://huggingface.co/McGill-NLP/AfriqueGemma-12B)</u> | <u>32.14</u> | <u>49.47</u> | <u>44.60</u> | <u>68.65</u> | <u>**66.89**</u> | <u>76.79</u> | <u>75.08</u> | <u>59.09</u> | <u>+4.8 (8.9%)</u> |
+| [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B-Base) | 11.22 | 36.56 | 38.24 | 44.63 | 18.93 | 29.47 | 53.06 | 33.16 | |
+| [AfriqueQwen-8B](https://huggingface.co/McGill-NLP/AfriqueQwen-8B) | 39.68 | 46.91 | 45.99 | 68.46 | 63.54 | 73.36 | 77.00 | 59.28 | +26.1 (78.8%) |
+| [Qwen3-14B-Base](https://huggingface.co/Qwen/Qwen3-14B-Base) | 16.60 | 39.66 | 43.22 | 50.74 | 20.86 | 41.80 | 66.29 | 39.88 | |
+| [AfriqueQwen-14B](https://huggingface.co/McGill-NLP/AfriqueQwen-14B) | **45.01** | **52.22** | **49.01** | **74.63** | 65.26 | **77.80** | **82.63** | **63.79** | +23.9 (60.0%) |
+| [Llama3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | 8.14 | 32.27 | 37.90 | 40.95 | 23.59 | 41.37 | 59.99 | 34.89 | |
+| [AfriqueLlama-8B](https://huggingface.co/McGill-NLP/AfriqueLlama-8B) | 17.51 | 36.57 | 37.39 | 50.51 | 64.88 | 71.17 | 69.14 | 49.60 | +14.7 (42.2%) |
+
+## Model Variants
+
+- [AfriqueGemma-4B](https://huggingface.co/McGill-NLP/AfriqueGemma-4B) - Smaller 4B variant
+- [AfriqueQwen-8B](https://huggingface.co/McGill-NLP/AfriqueQwen-8B) - Qwen-based 8B model
+- [AfriqueQwen-14B](https://huggingface.co/McGill-NLP/AfriqueQwen-14B) - Qwen-based 14B model (flagship)
+- [AfriqueLlama-8B](https://huggingface.co/McGill-NLP/AfriqueLlama-8B) - Llama-based 8B model
+
+## Citation
+
+If you find our work helpful, please cite:
+
+```bibtex
+@misc{yu2026afriquellmdatamixingmodel,
+      title={AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages}, 
+      author={Hao Yu and Tianyi Xu and Michael A. Hedderich and Wassim Hamidouche and Syed Waqas Zamir and David Ifeoluwa Adelani},
+      year={2026},
+      eprint={2601.06395},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2601.06395}, 
+}
+```
+
+## License
+
+This model is released under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/). Please review the license terms before use.
+
+## Acknowledgments
+
+We thank the creators of the base models, datasets and compute resources that made this work possible, including Mila, Compute Canada, Microsoft, the FineWeb team, WURA, MADLAD-400 and etc..