Building a 50M Parameter Turkish Language Model from Scratch

Introduction

At DemirözAI, our vision is to develop artificial intelligence models trained entirely on clean, original Turkish data. Our goal is to create native AI solutions suitable for both individual and enterprise needs. As the first step in this journey, we successfully developed a 50 million parameter Turkish conversational model trained from scratch. This article details the model's training process, techniques employed, and future application areas.

                Building language models for morphologically rich languages like Turkish presents unique challenges. Our approach focuses on leveraging Turkish's agglutinative nature while maintaining computational efficiency on consumer-grade hardware.
            

Data Collection and Preparation

The foundation of our model's success lies in carefully curated Turkish datasets. We compiled and processed data from diverse sources to ensure comprehensive language coverage:

Turkish Wikipedia: Encyclopedic, reliable content providing factual knowledge
TS Corpus and National Turkish Corpus: Extensive written texts covering various domains
OpenSubtitles: Conversational language structures from film subtitles
Forums and Q&A logs: Natural conversation data reflecting everyday language use

All data underwent rigorous preprocessing: conversion to UTF-8 standard, removal of HTML/Wiki markup, and filtering of extremely short or irrelevant sentences. This resulted in a completely Turkish, consistent, and clean training dataset.

Tokenizer Development

To enable the model to process Turkish text effectively, we trained a specialized Byte-Level BPE Tokenizer. This tokenizer encompasses approximately 50,000 subword units and is optimized for Turkish's agglutinative structure. The tokenizer enables the model to better understand Turkish morphology and generate fluent sentences.

from tokenizers import ByteLevelBPETokenizer

tokenizer = ByteLevelBPETokenizer()
tokenizer.train(
    files=turkish_corpus_files,
    vocab_size=50000,
    min_frequency=2,
    special_tokens=["<s>", "<pad>", "</s>", "<unk>", "<mask>"]
)
            

Model Architecture

Our model is a 50 million parameter language model inspired by the GPT-2 architecture. The configuration is as follows:

Number of layers: 6
Hidden dimension (embedding size): 512
Multi-head attention: 8 heads
Context window: 1024 tokens

This architecture enables the model to learn Turkish grammar effectively and generate fluent text in short to medium-length contexts.

Training Process

Training was conducted on an NVIDIA RTX 3050 Ti GPU using the Hugging Face Transformers library. We employed several optimization techniques to maximize efficiency:

Mixed Precision (FP16): Reducing memory usage and increasing training speed
Gradient Accumulation: Compensating for small batch sizes for efficient training
LoRA (Low-Rank Adaptation): Providing parameter efficiency during fine-tuning
Bitsandbytes 8-bit optimizers: Fast optimization with lower memory consumption

Training Phase	Token Count	Duration
Pre-training	50M tokens	10-20 hours
Fine-tuning	20M tokens	4-8 hours
RAG Integration	5M tokens	1-2 hours

Total training time was completed in approximately 1-2 days, demonstrating that effective language models can be developed even with limited computational resources.

Retrieval-Augmented Generation (RAG)

Beyond basic language knowledge, the model is enhanced with RAG (Retrieval-Augmented Generation) for current or domain-specific information. Our implementation includes:

Documents stored in a CPU-based vector database (FAISS, Milvus)
Relevant document retrieval based on user queries
Context injection for more accurate response generation

RAG enables the model to access information beyond its training data, significantly improving accuracy without requiring retraining.

                The RAG implementation allows us to keep the model size manageable while still providing access to vast amounts of information, making it practical for deployment on resource-constrained environments.
            

Performance and Use Cases

While a 50M parameter model has limited capacity compared to large-scale models, it performs effectively in several key areas:

Application Area	Performance Level
Simple Conversation	High - Fluent dialogue flow and sentence completion
Domain-Specific Content	Good - Effective for specific topics with fine-tuning
Language Tools	Good - Spell checking, synonym suggestions
Q&A Systems	Good - RAG-enhanced knowledge-based responses

Technical Optimizations

Training on consumer-grade hardware required careful optimization strategies:

Memory Management

With only 4-6GB VRAM available on the RTX 3050 Ti, we implemented aggressive memory optimization:

training_args = TrainingArguments(
    per_device_train_batch_size=1,
    gradient_accumulation_steps=4,
    fp16=True,
    optim="adamw_bnb_8bit",
    gradient_checkpointing=True
)
            

Parameter-Efficient Fine-Tuning

LoRA adaptation allowed us to fine-tune the model with minimal additional parameters:

Only low-rank matrices are trained during fine-tuning
Original model weights remain frozen
Dramatically reduces memory requirements
Maintains performance comparable to full fine-tuning

Future Roadmap

As DemirözAI, our goal is to develop larger and more powerful Turkish AI models building upon this foundation. Our near-term plans include:

100M+ parameter models: Scaling up for improved performance
Domain-specific chatbots: Specialized models for law, healthcare, and education
Enterprise RAG solutions: Custom knowledge bases for organizations
Multi-modal capabilities: Integrating vision and speech understanding

Lessons Learned

Through this project, we gained valuable insights into developing language models for morphologically rich languages:

                Key Insight: Quality of data matters more than quantity. Our carefully curated 50M tokens of clean Turkish text produced better results than larger but noisier datasets.
            

Tokenization is crucial: A well-designed tokenizer specifically for Turkish significantly improves model performance
Hardware limitations drive innovation: Constraints forced us to adopt cutting-edge optimization techniques
RAG is game-changing: Augmenting small models with retrieval dramatically expands their capabilities
Community datasets are valuable: Open-source Turkish corpora provided an excellent foundation

Conclusion

This Turkish conversational model developed from scratch by DemirözAI represents a significant step forward for native AI research. The model, developed with clean data, modern training techniques, and RAG integration, paves the way for AI solutions optimized for Turkish.

Despite the constraints of consumer-grade hardware and a relatively small parameter count, we've demonstrated that effective language models can be built for specific languages and use cases. Our experience shows that with the right techniques and optimizations, meaningful AI development is accessible even without massive computational resources.

This project serves as both a proof of concept and a foundation for future work in Turkish language AI. We believe that language-specific models, trained on high-quality native data, will play a crucial role in making AI technology more accessible and effective for Turkish speakers worldwide.

Tags:

#TurkishNLP #LanguageModels #GPT2 #RAG #LoRA #Transformers #HuggingFace #NLPTurkey #AIResearch #SmallModels #DemirözAI

demirozAI

Building a 50M Parameter Turkish Language Model from Scratch: Technical Journey and Implementation

Introduction

Data Collection and Preparation

Tokenizer Development

Model Architecture

Training Process

Retrieval-Augmented Generation (RAG)

Performance and Use Cases

Technical Optimizations

Memory Management

Parameter-Efficient Fine-Tuning

Future Roadmap

Lessons Learned

Conclusion

Tags:

Building a 50M Parameter Turkish Language Model from Scratch: Technical Journey and Implementation

Introduction

Data Collection and Preparation

Tokenizer Development

Model Architecture

Training Process

Retrieval-Augmented Generation (RAG)

Performance and Use Cases

Technical Optimizations

Memory Management

Parameter-Efficient Fine-Tuning

Future Roadmap

Lessons Learned

Conclusion

Tags:

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