🧠 Model Card: reuben256/nllb-distilled-600-lug

🌍 Overview

reuben256/nllb-distilled-600-lug is a fine-tuned version of Meta AI’s NLLB-200 distilled 600M model for English ↔ Luganda machine translation. It was developed by tekjuice AI 🧪 to support translation in low-resource African languages, specifically Luganda 🇺🇬 — a widely spoken Bantu language in Uganda.

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🚀 Use Cases

This model is designed for:

• 📚 Translating educational and public health materials
• 📰 Localizing government or NGO communications
• 🔬 Supporting linguistic and NLP research
• 🧩 Enabling cross-lingual tasks via translation (e.g., summarization, QA)

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📦 Training Data

Fine-tuned using the dataset reuben256/tekjuice-eng-lug-target, which includes:

• 📖 Public domain and open-source parallel corpora
• 🌍 Crowdsourced and community-translated sentences
• 🗞️ Aligned media and educational content

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📊 Evaluation

The model was evaluated using the BLEU metric 📘 to assess n-gram precision. Testing was done on a held-out set with similar domain characteristics as the training data.

⚠️ Note: Human evaluation is recommended for assessing fluency, nuance, and cultural accuracy.

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🏗️ Base Model

Built on top of:

• 🧬 facebook/nllb-200-distilled-600M — a distilled multilingual model optimized for speed and low-resource language performance.

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⚠️ Limitations

• ❌ May struggle with slang, idioms, and culturally specific phrases
• 📉 Biases in training data may be reflected in outputs
• 💡 Performance may degrade on out-of-domain or highly technical content

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🔮 Future Plans

Coming improvements:

• 📈 Larger and more diverse datasets
• 🔁 Reverse direction (Luganda → English)
• 🏥 Domain-specific fine-tuning (e.g., health, legal)
• 🧠 Quality estimation and confidence scoring

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🚀 How to Use

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

MODEL = "reuben256/nllb-distilled-600-lug"

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL)

tokenizer.src_lang = "eng_Latn"
tokenizer.tgt_lang = "lug_Latn"

text = "Farmers should plant more trees?"
inputs = tokenizer(text, return_tensors="pt")
translated_tokens = model.generate(**inputs)
print(tokenizer.batch_decode(translated_tokens, skip_special_tokens=True))