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Model: McGill-NLP/AfriqueGemma-4B Source: Original Platform
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README.md
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---
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library_name: transformers
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license: cc-by-4.0
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base_model: google/gemma-3-4b-pt
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language:
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- af # Afrikaans
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- am # Amharic
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- ar # Arabic
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- en # English
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- fr # French
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- ha # Hausa
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- ig # Igbo
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- mg # Malagasy (Plateau)
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- ny # Nyanja
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- om # Oromo
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- pt # Portuguese
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- rw # Kinyarwanda
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- sn # Shona
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- so # Somali
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- st # Southern Sotho
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- sw # Swahili
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- ti # Tigrinya
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- tn # Tswana
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- xh # Xhosa
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- yo # Yoruba
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- zu # Zulu
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pipeline_tag: text-generation
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tags:
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- african-languages
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- multilingual
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- continued-pretraining
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- afrique-llm
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- gemma3
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- image-text-to-text
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- gemma
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- llamafactory
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---
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# AfriqueGemma-4B
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## Model Overview
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**AfriqueGemma-4B** is part of the **AfriqueLLM** suite, a collection of open language models adapted to **20 African languages** through continued pre-training (CPT) on **~26B tokens**. This model is based on [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt) and has been specifically adapted for improved performance on African languages while maintaining strong capabilities in high-resource languages.
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### Key Features
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- **Type**: Causal Language Model (Base/Pre-trained)
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- **Base Model**: Gemma 3 4B PT
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- **Parameters**: 4B
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- **Context Length**: 8,192 tokens (native)
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- **Training Tokens**: ~26B tokens of carefully curated multilingual data
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## Supported Languages
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AfriqueGemma-4B has been adapted for the following 20 African languages:
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| Language | Code | Family | Script |
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|----------|------|--------|--------|
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| Afrikaans | afr_Latn | Germanic | Latin |
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| Swahili | swh_Latn | Bantu | Latin |
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| Moroccan Arabic | ary_Arab | Semitic | Arabic |
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| Somali | som_Latn | Cushitic | Latin |
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| Amharic | amh_Ethi | Semitic | Ethiopic |
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| Egyptian Arabic | arz_Arab | Semitic | Arabic |
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| Hausa | hau_Latn | Chadic | Latin |
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| Kinyarwanda | kin_Latn | Bantu | Latin |
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| Zulu | zul_Latn | Bantu | Latin |
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| Igbo | ibo_Latn | Volta-Niger | Latin |
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| Plateau Malagasy | plt_Latn | Austronesian | Latin |
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| Xhosa | xho_Latn | Bantu | Latin |
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| Shona | sna_Latn | Bantu | Latin |
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| Yoruba | yor_Latn | Volta-Niger | Latin |
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| Nyanja | nya_Latn | Bantu | Latin |
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| Southern Sotho | sot_Latn | Bantu | Latin |
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| Tigrinya | tir_Ethi | Semitic | Ethiopic |
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| Tunisian Arabic | aeb_Arab | Semitic | Arabic |
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| Oromo | gaz_Latn | Cushitic | Latin |
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| Tswana | tsn_Latn | Bantu | Latin |
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**High-resource languages used for catastrophic forgetting mitigation:** English, French, Portuguese, Arabic
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## Training Data
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Our training corpus combines multiple high-quality sources:
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- **African Monolingual Data** (~22.8B tokens): FineWeb2, WURA, and MADLAD-400
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- **Code** (~1B tokens): CornStack-Python for reasoning capabilities
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- **Mathematics** (~1B tokens): FineMath-4+ for mathematical understanding
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- **Synthetic Data** (~324M tokens): GPT-4.1 translated domain-specific content across 10 domains
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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.
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## Quickstart
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "McGill-NLP/AfriqueGemma-4B"
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# Load the tokenizer and the model
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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# Prepare the model input
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prompt = "Bawo ni o ṣe n ṣe?" # Yoruba: "How are you doing?"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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# Generate text
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generated_ids = model.generate(
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**inputs,
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max_new_tokens=100,
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)
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output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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print(output)
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```
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## Deployment
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For deployment, you can use `vllm` or `sglang` to create an OpenAI-compatible API endpoint:
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**vLLM:**
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```shell
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vllm serve McGill-NLP/AfriqueGemma-4B
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```
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**SGLang:**
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```shell
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python -m sglang.launch_server --model-path McGill-NLP/AfriqueGemma-4B
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```
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## Training Details
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### Hyperparameters
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- **Learning Rate**: 5e-5 (with warmup and cosine decay)
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- **Context Length**: 16,384 tokens
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- **Optimizer**: AdamW
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- **Precision**: BF16 mixed precision
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### Infrastructure
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Training was conducted using the LLaMA-Factory framework on up to 64 NVIDIA H100 GPUs with:
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- DeepSpeed ZeRO-1/ZeRO-2
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- Flash Attention 3
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- Sequence packing
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- Liger Kernel optimizations
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## Evaluation
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All AfriqueLLM models are evaluated on multiple multilingual benchmarks. FLORES is reported only in the English-to-target direction (`eng->xxx`):
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| Model | AfriMGSM | AfriMMLU | AfriXNLI | Belebele | FLORES (eng->xxx) | INJONG | SIB-200 | Overall | Δ (Δ %) |
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|-------|----------|----------|----------|----------|--------|--------|---------|---------|---|
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| [Gemma3-4B](https://huggingface.co/google/gemma-3-4b-pt) | 10.24 | 33.89 | 37.76 | 45.79 | 35.36 | 55.52 | 63.59 | 40.31 | |
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| <u>[AfriqueGemma-4B](https://huggingface.co/McGill-NLP/AfriqueGemma-4B)</u> | <u>14.86</u> | <u>36.73</u> | <u>39.62</u> | <u>50.52</u> | <u>54.95</u> | <u>69.28</u> | <u>69.21</u> | <u>47.88</u> | <u>+7.6 (18.8%)</u> |
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| [Gemma3-12B](https://huggingface.co/google/gemma-3-12b-pt) | 25.21 | 48.76 | 44.01 | 68.84 | 44.09 | 73.53 | 79.17 | 54.80 | |
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| [AfriqueGemma-12B](https://huggingface.co/McGill-NLP/AfriqueGemma-12B) | 32.14 | 49.47 | 44.60 | 68.65 | 65.04 | 76.79 | 75.08 | 58.82 | +4.0 (7.3%) |
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| [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B-Base) | 8.26 | 33.84 | 37.12 | 41.50 | 20.16 | 21.69 | 57.88 | 31.49 | |
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| [AfriqueQwen-4B](https://huggingface.co/McGill-NLP/AfriqueQwen-4B) | 33.09 | 43.04 | 44.88 | 63.62 | 59.82 | 65.34 | 74.77 | 54.94 | +23.4 (74.4%) |
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| [Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B-Base) | 20.79 | 38.63 | 40.36 | 55.82 | 32.06 | 59.43 | 74.96 | 46.01 | |
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| [AfriqueQwen3.5-4B](https://huggingface.co/McGill-NLP/AfriqueQwen3.5-4B) | 30.47 | 43.66 | 41.05 | 66.01 | 63.55 | 75.46 | 79.66 | 57.12 | +11.1 (24.2%) |
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| [AfriqueQwen3.5-4B-ExtendedCM](https://huggingface.co/McGill-NLP/AfriqueQwen3.5-4B-ExtendedCM) | 34.17 | 45.26 | 41.94 | 66.45 | 63.76 | 75.97 | 80.52 | 58.30 | +1.2 (2.1%) |
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| [AfriqueQwen3.5-4B-50Langs](https://huggingface.co/McGill-NLP/AfriqueQwen3.5-4B-50Langs) | 34.06 | 45.23 | 41.79 | 66.83 | 64.56 | 75.82 | 79.83 | 58.30 | +0.0 (0.0%) |
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| [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B-Base) | 11.22 | 36.56 | 38.24 | 44.63 | 21.13 | 29.47 | 53.06 | 33.47 | |
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| [AfriqueQwen-8B](https://huggingface.co/McGill-NLP/AfriqueQwen-8B) | 39.68 | 46.91 | 45.99 | 68.46 | 62.18 | 73.36 | 77.00 | 59.08 | +25.6 (76.5%) |
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| [Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B-Base) | 16.60 | 39.66 | 43.22 | 50.74 | 23.61 | 41.80 | 66.29 | 40.27 | |
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| **[AfriqueQwen-14B](https://huggingface.co/McGill-NLP/AfriqueQwen-14B)** | **45.01** | **52.22** | **49.01** | **74.63** | **63.77** | **77.80** | **82.63** | **63.58** | **+23.3 (57.9%)** |
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| [Llama3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | 8.14 | 32.27 | 37.90 | 40.95 | 26.69 | 41.37 | 59.99 | 35.33 | |
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| [AfriqueLlama-8B](https://huggingface.co/McGill-NLP/AfriqueLlama-8B) | 17.51 | 36.57 | 37.39 | 50.51 | 63.60 | 71.17 | 69.14 | 49.41 | +14.1 (39.9%) |
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| [Lugha-Llama-8B-wura](https://huggingface.co/Lugha/Meta-Llama-3.1-8B-wura) | 9.46 | 37.00 | 39.24 | 47.86 | 49.90 | 62.30 | 75.81 | 45.94 | |
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| [Gemma3-27B](https://huggingface.co/google/gemma-3-27b-pt) | 35.37 | 55.47 | 46.85 | 74.81 | 48.41 | 79.70 | 84.34 | 60.71 | |
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### Additional-Language Evaluation
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This table averages only evaluated African languages outside the first 20-language CPT set: Ewe, Lingala, Ganda, Twi, and Wolof. Benchmark cells average the available languages for that benchmark; FLORES is English-to-target only (`eng->xxx`).
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| Model | AfriMGSM | AfriMMLU | AfriXNLI | Belebele | FLORES (eng->xxx) | INJONG | SIB-200 | Avg | Δ (Δ %) |
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|-------|----------|----------|----------|----------|-------------------|--------|---------|-----|--------|
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| [Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B-Base) | 8.30 | 32.35 | 34.30 | 36.90 | 22.20 | 33.27 | 58.96 | 32.33 | |
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| [AfriqueQwen3.5-4B](https://huggingface.co/McGill-NLP/AfriqueQwen3.5-4B) | 7.52 | 31.17 | 33.56 | 35.82 | 26.28 | 33.10 | 56.66 | 32.02 | -0.31 (-1.0%) |
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| [AfriqueQwen3.5-4B-ExtendedCM](https://huggingface.co/McGill-NLP/AfriqueQwen3.5-4B-ExtendedCM) | 8.13 | 32.72 | 33.74 | 36.76 | 24.06 | 32.41 | 56.00 | 31.97 | -0.05 (-0.2%) |
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| [AfriqueQwen3.5-4B-50Langs](https://huggingface.co/McGill-NLP/AfriqueQwen3.5-4B-50Langs) | 21.07 | 37.65 | 36.31 | 51.56 | 56.33 | 61.37 | 75.75 | 48.58 | +16.61 (+52.0%) |
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## Model Variants
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- [AfriqueQwen-14B](https://huggingface.co/McGill-NLP/AfriqueQwen-14B) - Qwen-based 14B model (flagship)
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- [AfriqueQwen-8B](https://huggingface.co/McGill-NLP/AfriqueQwen-8B) - Qwen-based 8B model
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- [AfriqueQwen-4B](https://huggingface.co/McGill-NLP/AfriqueQwen-4B) - Qwen-based 4B model
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- [AfriqueQwen3.5-4B](https://huggingface.co/McGill-NLP/AfriqueQwen3.5-4B) - Qwen 3.5-based 4B model
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- [AfriqueQwen3.5-4B-ExtendedCM](https://huggingface.co/McGill-NLP/AfriqueQwen3.5-4B-ExtendedCM) - Qwen 3.5-based 4B model with extended continued pre-training
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- [AfriqueQwen3.5-4B-50Langs](https://huggingface.co/McGill-NLP/AfriqueQwen3.5-4B-50Langs) - Qwen 3.5-based 4B model with 50-language coverage
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- [AfriqueGemma-12B](https://huggingface.co/McGill-NLP/AfriqueGemma-12B) - Gemma-based 12B model
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- [AfriqueLlama-8B](https://huggingface.co/McGill-NLP/AfriqueLlama-8B) - Llama-based 8B model
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## Citation
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|
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If you find our work helpful, please cite:
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|
||||
```bibtex
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@misc{yu2026afriquellmdatamixingmodel,
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title={AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages},
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author={Hao Yu and Tianyi Xu and Michael A. Hedderich and Wassim Hamidouche and Syed Waqas Zamir and David Ifeoluwa Adelani},
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year={2026},
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eprint={2601.06395},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2601.06395},
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}
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```
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## 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.
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## Acknowledgments
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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..
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added_tokens.json
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{
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"<image_soft_token>": 262144
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}
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{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ content }}{% elif message['role'] == 'assistant' %}{{ content }}{% endif %}{% endfor %}
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config.json
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{
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"architectures": [
|
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"Gemma3ForConditionalGeneration"
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],
|
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"boi_token_index": 255999,
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"eoi_token_index": 256000,
|
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"hidden_size": 2560,
|
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"image_token_index": 262144,
|
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"initializer_range": 0.02,
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"mm_tokens_per_image": 256,
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"model_type": "gemma3",
|
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"text_config": {
|
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"_sliding_window_pattern": 6,
|
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
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"attn_logit_softcapping": null,
|
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"final_logit_softcapping": null,
|
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"head_dim": 256,
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 10240,
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"layer_types": [
|
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"sliding_attention",
|
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"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention"
|
||||
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37
preprocessor_config.json
Normal file
37
preprocessor_config.json
Normal file
@@ -0,0 +1,37 @@
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{
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}
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4
processor_config.json
Normal file
4
processor_config.json
Normal file
@@ -0,0 +1,4 @@
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{
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33
special_tokens_map.json
Normal file
33
special_tokens_map.json
Normal file
@@ -0,0 +1,33 @@
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{
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
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version https://git-lfs.github.com/spec/v1
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oid sha256:4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795
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size 33384568
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3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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size 4689074
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51347
tokenizer_config.json
Normal file
51347
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user