Bengali LLM Pipeline - Complete Guide

A production-ready Bengali Language Model pipeline built with Python, Hugging Face Transformers, and PyTorch.

🎯 What This Project Does

This pipeline helps you:

1. Load & Clean Bengali text data
2. Tokenize text (convert to numbers)
3. Train a Bengali language model
4. Fine-tune on your custom data
5. Generate Bengali text from prompts
6. Instruction-tune for Q&A tasks

Think of it like creating your own Bengali ChatGPT, starting from scratch!

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📁 Project Structure

sleekAI/
├── data/                          # Data storage
│   ├── raw/                       # Raw Bengali text
│   └── processed/                 # Cleaned data (JSONL format)
├── models/                        # Trained models
│   ├── tokenizer/                 # Tokenizer files
│   ├── checkpoints/               # Training checkpoints
│   └── final_model/               # Final trained model
├── scripts/                       # Python scripts
│   ├── prepare_data.py            # Data cleaning
│   ├── setup_tokenizer.py         # Tokenizer setup
│   └── instruction_tuning.py      # Instruction tuning
├── notebooks/                     # Jupyter notebooks (optional)
├── config.py                      # Configuration (ONE PLACE TO CHANGE SETTINGS!)
├── train.py                       # Main training script
├── inference.py                   # Generate Bengali text
├── requirements.txt               # Python dependencies
└── README.md                      # This file

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🚀 Quick Start (5 minutes)

1. Install Dependencies

pip install -r requirements.txt

2. Prepare Data

python scripts/prepare_data.py

Output: data/processed/bengali_texts.jsonl

3. Setup Tokenizer

python scripts/setup_tokenizer.py

4. Train the Model

python train.py

⏱️ On CPU: ~15-30 minutes
⏱️ On GPU: ~2-5 minutes

5. Generate Bengali Text

python inference.py

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🎓 Understanding the Pipeline

For Backend Developers (PHP/Laravel Background)

ML Concept	Web Analogy
Dataset	Database with millions of rows
Tokenizer	URL encoder (text → numbers)
Model	Cache + prediction engine
Training	Learning patterns from data
Inference	Serving predictions via API
config.py	.env file (all settings)

Data Flow

Raw Text → Clean → Tokenize → Train Model → Save → Generate

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⚙️ Configuration

All settings in config.py:

BATCH_SIZE = 8              # Adjust for memory
NUM_EPOCHS = 3              # More training
LEARNING_RATE = 2e-5        # Learning speed
MAX_SEQ_LENGTH = 512        # Context length
USE_FP16 = False            # GPU acceleration

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📊 Step-by-Step Explanation

Step 1: Data Preparation (prepare_data.py)

Cleans Bengali text:

• Removes URLs, emails
• Fixes Unicode encoding
• Removes extra spaces
• Filters invalid texts
• Saves as JSONL

Input: Raw Bengali text
Output: bengali_texts.jsonl

Step 2: Tokenization (setup_tokenizer.py)

Converts text to numbers:

• Input: "বাংলাদেশ"
• Output: [1234, 5678, 9012, 3456]

Options:

• Pretrained (recommended): AI4Bharat's Indic-BERT
• Custom: Train on your data

Step 3: Model Training (train.py)

Teaches model to predict Bengali:

1. Load pretrained model
2. Fine-tune on your data
3. Save checkpoints
4. Evaluate on validation set

Optimization:

• Gradient accumulation
• FP16 (on GPU)
• Gradient checkpointing

Step 4: Inference (inference.py)

Generate Bengali text:

• Load trained model
• Take Bengali prompt
• Generate token-by-token
• Output Bengali text

Bonus: Instruction Tuning

Teach Q&A capabilities:

Input:  "প্রশ্ন: বাংলাদেশ কোথায় অবস্থিত?"
Output: "উত্তর: বাংলাদেশ দক্ষিণ এশিয়ায় অবস্থিত।"

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🔌 Hardware Requirements

CPU Only

• Works fine
• 15-30 minutes per epoch
• 8GB+ RAM needed

GPU (CUDA)

• Much faster
• 2-5 minutes per epoch
• 4GB+ VRAM needed

Check GPU availability:

python -c "import torch; print(torch.cuda.is_available())"

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📝 Customization Examples

Use Your Own Data

# 1. Put Bengali texts in data/raw/my_texts.txt
# 2. Edit prepare_data.py to load from your file
# 3. Run pipeline
python scripts/prepare_data.py
python train.py

Different Base Model

# In config.py
BASE_MODEL = "xlm-roberta-base"  # Multilingual

Longer Training

# In config.py
NUM_EPOCHS = 10
LEARNING_RATE = 1e-5

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🐛 Troubleshooting

Out of Memory

# config.py
BATCH_SIZE = 4
GRADIENT_ACCUMULATION_STEPS = 2
GRADIENT_CHECKPOINTING = True

Slow on CPU

That's normal. Options:

• Use GPU
• Reduce MAX_SEQ_LENGTH to 256
• Process less data

Poor Results

• Get more data
• Train longer (NUM_EPOCHS = 10)
• Use better base model

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🏆 Model Recommendations

For Bengali Only (Best)

BASE_MODEL = "ai4bharat/indic-bert"

Fast, Bengali-optimized, 110M parameters

For Multilingual

BASE_MODEL = "xlm-roberta-base"

Works for many languages, 270M parameters

For Quick Testing

BASE_MODEL = "distilgpt2"

Very small, 82M parameters

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📖 Resources

• Hugging Face Transformers
• PyTorch Docs
• Bengali NLP
• AI4Bharat

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❓ FAQ

Q: Production-ready?
A: Yes! Add error handling and monitoring for production.

Q: How to get more data?
A: OSCAR, Wikipedia, web scraping, HuggingFace datasets

Q: How to improve results?
A: More data, longer training, better base model, task-specific fine-tuning

Q: Can I use as a library?
A: Yes!

from inference import BengaliLLMInference
model = BengaliLLMInference()
output = model.generate("বাংলাদেশ")

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Happy training! 🚀