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Model: McGill-NLP/AfriqueQwen-4B Source: Original Platform
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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: Qwen/Qwen3-4B-Base
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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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- qwen3
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- qwen
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- llamafactory
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---
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# AfriqueQwen-4B
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## Model Overview
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**AfriqueQwen-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 [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base) and has been specifically adapted for improved performance on African languages while maintaining strong capabilities in high-resource languages.
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Our experiments show that **Qwen 3 models achieve the best performance** among all base models tested, better preserving performance in high-resource languages after CPT and achieving strong results on long-context tasks such as document-level translation.
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### Key Features
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- **Type**: Causal Language Model (Base/Pre-trained)
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- **Base Model**: Qwen 3 4B
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- **Parameters**: 4B
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- **Context Length**: 32,768 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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AfriqueQwen-4B has been adapted for the following 20 African languages plus 4 high-resource 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 (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/AfriqueQwen-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/AfriqueQwen-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/AfriqueQwen-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:
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| Model | AfriMGSM | AfriMMLU | AfriXNLI | Belebele | FLORES | INJONG | SIB-200 | Overall | 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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| [AfriqueGemma-4B](https://huggingface.co/McGill-NLP/AfriqueGemma-4B) | 14.86 | 36.73 | 39.62 | 50.52 | 54.95 | 69.28 | 69.21 | 47.88 | +7.6 (18.8%) |
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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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| <u>[AfriqueQwen-4B](https://huggingface.co/McGill-NLP/AfriqueQwen-4B)</u> | <u>33.09</u> | <u>43.04</u> | <u>44.88</u> | <u>63.62</u> | <u>59.82</u> | <u>65.34</u> | <u>74.77</u> | <u>54.94</u> | <u>+23.4 (74.4%)</u> |
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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 | 63.51 | 75.97 | 80.52 | 58.26 | +1.1 (2.0%) |
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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 | 23.75 | 41.80 | 66.29 | 40.29 | |
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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 (57.8%)** |
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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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## 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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- [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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- [AfriqueGemma-4B](https://huggingface.co/McGill-NLP/AfriqueGemma-4B) - Gemma-based 4B model
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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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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
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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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"</think>": 151668,
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"</tool_call>": 151658,
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
|
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{{- messages[0].content + '\n\n' }}
|
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{%- endif %}
|
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
|
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{{- "\n" }}
|
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
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{%- else %}
|
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
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{%- endif %}
|
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
|
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{%- set index = (messages|length - 1) - loop.index0 %}
|
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{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
|
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{%- endif %}
|
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{%- endfor %}
|
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{%- for message in messages %}
|
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set content = message.content %}
|
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
|
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{%- if '</think>' in message.content %}
|
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{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
|
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{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- endif %}
|
||||
{%- endif %}
|
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{%- if loop.index0 > ns.last_query_index %}
|
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{%- if loop.last or (not loop.last and reasoning_content) %}
|
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
68
config.json
Normal file
68
config.json
Normal file
@@ -0,0 +1,68 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151643,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2560,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 9728,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
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|
||||
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|
||||
"full_attention",
|
||||
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|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.55.4",
|
||||
"use_cache": false,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151643,
|
||||
"max_new_tokens": 2048,
|
||||
"transformers_version": "4.55.4"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
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3
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407
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Normal file
407
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Normal file
@@ -0,0 +1,407 @@
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{
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
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|
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
|
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size 11422654
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240
tokenizer_config.json
Normal file
240
tokenizer_config.json
Normal file
@@ -0,0 +1,240 @@
|
||||
{
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user