This model card provides detailed information about the LLaMA-3.1-8B model fine-tuned for South African languages. The model demonstrates cost-effective cross-lingual transfer learning for African language processing.

Model Overview

The model is based on Meta's LLaMA-3.1-8B-Instruct architecture and has been fine-tuned on translated versions of the Alpaca Cleaned dataset. The training approach leverages machine translation to create instruction-tuning data in five South African languages, making it a cost-effective solution for multilingual AI development.

Training Methodology

Dataset Preparation

The training data was created by translating the Alpaca Cleaned dataset into five target languages:

Xhosa
Zulu
Tswana
Northern Sotho
Afrikaans

Machine translation was used to generate the training data, with a cost of $370 per language.

Training Process

The model was trained using the PEFT (Parameter-Efficient Fine-Tuning) library on the Akash Compute Network. Key aspects of the training process include:

Single epoch training
Multi-GPU distributed training setup
Cosine learning rate schedule with 10% warmup
Adam optimizer with β1=0.9, β2=0.999, ε=1e-08
Total training cost: $15

Performance Evaluation

Evaluation Scope

Current evaluation metrics are available for two languages:

Xhosa (xho)
Zulu (zul)

Evaluation was conducted using three benchmark datasets:

AfriMGSM Results

Xhosa: 2.0% accuracy
Zulu: 4.5% accuracy

AfriMMIU Results

Xhosa: 29.0% accuracy
Zulu: 29.0% accuracy

AfriXNLI Results

Xhosa: 44.0% accuracy
Zulu: 43.0% accuracy

Limitations and Considerations

Evaluation Coverage
- Only Xhosa and Zulu could be evaluated due to limitations in available benchmarking tools
- Performance on other supported languages remains unknown
Training Data Quality
- Reliance on machine translation may impact the quality of training data
- Potential artifacts or errors from the translation process could affect model performance
Performance Gaps
- Notably low performance on AfriMGSM tasks indicates room for improvement
- Further investigation needed to understand performance disparities across tasks

Technical Requirements

The model requires the following framework versions:

PyTorch: 2.4.1+cu121
Transformers: 4.44.2
PEFT: 0.12.0
Datasets: 3.0.0
Tokenizers: 0.19.1

Usage Example

python

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "meta-llama/llama-8b-south-africa"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage for text generation
text = "Translate to Xhosa: Hello, how are you?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

License

This model is released under the Apache 2.0 license. The full license text can be found at https://www.apache.org/licenses/LICENSE-2.0.txt

Acknowledgments

Meta AI for the base LLaMA-3.1-8B-Instruct model
Akash Network for providing computing resources
Contributors to the Alpaca Cleaned dataset
The African NLP community for benchmark datasets and evaluation tools

africa-intelligence/llama-8b-south-africa

LLaMA-3.1-8B South African Languages Model