Akai LLM: Practical Exploration of Building an Open-Source Turkish Large Language Model from Scratch

The Akai project is an initiative to build an open-source Turkish-focused large language model from scratch. It aims to address the lag in AI model capabilities and ecosystems for non-English (especially low-resource) languages under English dominance, provide valuable practical experience for the development of large models for low-resource languages, and promote linguistic diversity and technological inclusion.

Background and Motivation

In the global development of LLMs, English dominates, while medium-resource languages like Turkish lag behind, creating a digital divide. Akai chose to develop independently from scratch instead of fine-tuning existing multilingual models.

Challenges of Turkish

1. Complex Language Structure: Turkic language family with agglutinative grammar; affix stacking leads to vocabulary explosion, long-distance dependencies, and morphological complexity;
2. Scarce Data Resources: Lack of high-quality digitized texts, insufficient annotated data, and uneven domain coverage;
3. Limited Technical Ecosystem: Poor adaptability of existing tools for Turkish, lack of evaluation benchmarks, and limited community support.

Tokenization Strategy

Optimize BPE algorithm to adapt to Turkish affix structure, and introduce morphology-aware preprocessing to inject linguistic priors.

Model Architecture

Choose a moderately sized Transformer, improve attention mechanisms (sliding window/sparse attention), and adopt multi-stage training: pre-training → domain adaptation → instruction fine-tuning.

Data Engineering

Collect diverse corpora (web pages, public datasets, etc.), perform strict cleaning (deduplication, toxicity detection, etc.), and strategically use synthetic data to expand instruction fine-tuning data.

Open-Source Content

Publicly release training code (PyTorch distributed training), pre-trained model checkpoints, data processing tools, and Turkish evaluation benchmarks.

Community Participation

Interact through channels like GitHub to receive bug reports, explore application scenarios, and share knowledge, driving project iteration.

1. Contribution to Low-Resource Languages: Prove that practical models can be built with limited resources, providing a reference path for medium-resource languages like Thai and Vietnamese;
2. Linguistic Diversity: Preserve cultural heritage and identity, and promote inclusive AI technology;
3. Open-Source Ecosystem: Enrich the selection of non-English models and provide a research platform for low-resource language models.

Current Limitations

Restricted scale, insufficient data coverage, and immature evaluation benchmarks.

Future Directions

Expand model scale, explore multimodal capabilities, enhance tool usage and Agent capabilities, and improve community contribution mechanisms.