Hugging Face model: africa-intelligence/llama-8b-south-africa
Author: africa-intelligence
Created: 2024-09-08 18:48:48
Last modified: 2025-01-18 13:13:09
Library: peft
Likes: 3 | Downloads: 17
Tags: peft, tensorboard, safetensors, llama, african-languages, multilingual, instruction-tuning, transfer-learning, base_model:meta-llama/Llama-3.1-8B-Instruct, base_model:adapter:meta-llama/Llama-3.1-8B-Instruct, license:apache-2.0, 4-bit, bitsandbytes, region:us
Base model relation: adapter
Base models: id: meta-llama/Llama-3.1-8B-Instruct
Card metadata:
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
model_name: llama-8b-south-africa
languages: Xhosa, Zulu, Tswana, Northern Sotho, Afrikaans
license: apache-2.0
tags: african-languages, multilingual, instruction-tuning, transfer-learning
library_name: peft
model_description: This model is a fine-tuned version of Meta's LLaMA-3.1-8B-Instruct model, specifically adapted for South African languages. The training data consists of the Alpaca Cleaned dataset translated into five South African languages: Xhosa, Zulu, Tswana, Northern Sotho, and Afrikaans using machine translation techniques.

Key Features:
- Base architecture: LLaMA-3.1-8B-Instruct
- Training approach: Instruction tuning via translated datasets
- Target languages: 5 South African languages
- Cost-efficient: Total cost ~$1,870 ($370/language for translation + $15 for training)

training_details: hyperparameters: learning_rate: 0.0002
train_batch_size: 4
eval_batch_size: 8
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1
seed: 42
distributed_type: multi-GPU
results: final_loss: 1.0959
validation_loss: 0.0571
total_steps: 5596
completed_epochs: 0.9999
model_evaluation: xhosa: afrimgsm: accuracy: 0.02
afrimmlu: accuracy: 0.29
afrixnli: accuracy: 0.44
zulu: afrimgsm: accuracy: 0.045
afrimmlu: accuracy: 0.29
afrixnli: accuracy: 0.43
limitations: - Current evaluation metrics are limited to Xhosa and Zulu due to Iroko language availability
- Machine translation was used for training data generation, which may impact quality
- Low performance on certain tasks (particularly AfriMGSM) suggests room for improvement

framework_versions: pytorch: 2.4.1+cu121
transformers: 4.44.2
peft: 0.12.0
datasets: 3.0.0
tokenizers: 0.19.1
resources: benchmark_visualization: assets/Benchmarks_(1).pdf
training_dataset: https://huggingface.co/datasets/yahma/alpaca-cleaned