Instructions to use McGill-NLP/AfriqueQwen-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use McGill-NLP/AfriqueQwen-8B with Transformers:

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

pipe = pipeline("text-generation", model="McGill-NLP/AfriqueQwen-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/AfriqueQwen-8B")
model = AutoModelForCausalLM.from_pretrained("McGill-NLP/AfriqueQwen-8B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use McGill-NLP/AfriqueQwen-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "McGill-NLP/AfriqueQwen-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "McGill-NLP/AfriqueQwen-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/McGill-NLP/AfriqueQwen-8B

SGLang

How to use McGill-NLP/AfriqueQwen-8B 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 "McGill-NLP/AfriqueQwen-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "McGill-NLP/AfriqueQwen-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "McGill-NLP/AfriqueQwen-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "McGill-NLP/AfriqueQwen-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use McGill-NLP/AfriqueQwen-8B with Docker Model Runner:
```
docker model run hf.co/McGill-NLP/AfriqueQwen-8B
```

AfriqueQwen-8B / README.md

PeterYu

Upload folder using huggingface_hub

b220795 verified 7 months ago

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library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-8B
language:
  - af
  - am
  - ar
  - en
  - fr
  - ha
  - ig
  - mg
  - ny
  - om
  - pt
  - rw
  - sn
  - so
  - st
  - sw
  - ti
  - tn
  - xh
  - yo
  - zu
pipeline_tag: text-generation
tags:
  - african-languages
  - multilingual
  - continued-pretraining
  - afrique-llm
  - qwen

AfriqueQwen-8B

AfriqueLLM Evaluation Results

Model Overview

AfriqueQwen-8B 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/Qwen3-8B and has been specifically adapted for improved performance on African languages while maintaining strong capabilities in high-resource languages.

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.

Key Features

Type: Causal Language Model (Base/Pre-trained)
Base Model: Qwen 3 8B
Parameters: 8B
Context Length: 32,768 tokens (native)
Training Tokens: 26B tokens of carefully curated multilingual data

Supported Languages

AfriqueQwen-8B 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
Parallel Data (~456M tokens): NLLB-OPUS filtered with SSA-COMET (threshold 0.7)

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

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "McGill-NLP/AfriqueQwen-8B"

# 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:

vllm serve McGill-NLP/AfriqueQwen-8B

SGLang:

python -m sglang.launch_server --model-path McGill-NLP/AfriqueQwen-8B

Training Details

Hyperparameters

Learning Rate: 5e-5 (with warmup and cosine decay)
Context Length: 8,192 tokens (training)
Batch Size: Effective batch size optimized for throughput
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

AfriqueQwen-8B is evaluated on multiple multilingual benchmarks including:

AfriMGSM: Mathematical reasoning
AfriMMLU: Multilingual knowledge
AfriXNLI: Natural language inference
Belebele: Reading comprehension
SIB-200: Topic classification
FLORES: Machine translation

Model Variants

AfriqueQwen-14B - Larger, more capable variant

Intended Use

This model is designed for:

Research on African language NLP
Building applications for African language communities
Cross-lingual transfer learning experiments
Multilingual text generation and understanding

Limitations

This is a base/pre-trained model and may require fine-tuning for specific tasks
Performance varies across languages based on data availability
May generate biased or inappropriate content without proper safeguards
Not suitable for production use without additional safety measures

Citation

If you find our work helpful, please cite:

@article{afriquellm2025,
  title={AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages},
  author={AfriqueLLM Team},
  year={2025}
}

License

This model is released under the Apache 2.0 License. Please review the license terms before use.

Acknowledgments

We thank the creators of the base models and datasets that made this work possible, including Alibaba (Qwen), the FineWeb team, WURA, MADLAD-400, and the NLLB project.