NLLB-Twi-Human-Aligned

NLLB-Twi Human-Aligned Translator (Phase 2)

This repository contains a QLoRA adapter for the NLLB-200 (600M) model, specifically fine-tuned for high-fidelity Twi-to-English translation. This model is the result of a two-stage alignment framework designed to achieve state-of-the-art performance on low-resource hardware.

Performance Metrics

Note: Evaluation was conducted on a held-out set of 500 sentences from the GhanaNLP Pristine corpus.

Training Methodology

The model was developed using a two-stage curriculum to balance structural accuracy with conversational naturalness:

1. Phase 1: Synthetic Scaling: Initial training on 192,000 parallel sentences from the GhanaNLP Pristine corpus. This phase established the foundational morphological and syntactic patterns of Twi.
2. Phase 2: Human Alignment: Refinement on 4,331 human-verified sentence pairs. This stage addressed the "robotic" stylistic artifacts typical of synthetic data, aligning the model with natural Twi phrasing.

Technical Specifications

• Hardware: NVIDIA RTX 2060 (6GB VRAM).
• Quantization: 4-bit NormalFloat (NF4) via bitsandbytes.
• Methodology: QLoRA (Rank 16, Alpha 32).
• Optimizer: Paged AdamW (8-bit).
• Learning Rate: 2e-4 (Phase 1) reduced to 5e-5 (Phase 2) for conservative alignment.

Usage

The model can be loaded directly using the Transformers library. The base NLLB model weights will be inferred and loaded automatically from the adapter configuration.

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

model_id = "mclanorjeff/NLLB-Twi-Human-Aligned"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, src_lang="aka_GH")
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

# Inference
text = "Meresua Twi kasa kyerɛ wo."
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    output_ids = model.generate(
        **inputs, 
        forced_bos_token_id=tokenizer.convert_tokens_to_ids("eng_Latn"),
        max_length=128,
        num_beams=5
    )

print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

Intended Use

This model is intended for research and development in African NLP. It is optimized for Twi-to-English translation across various domains, including conversational and formal text.

Acknowledgements

We acknowledge the GhanaNLP community for providing the foundational datasets that made this research possible.