5 posts
What is an LLM? How do Large Language Models (LLMs) work, what does Transformer architecture solve, what are tokens, embeddings, and context windows, and how do GPT-5, Claude Opus 4.7, Gemini 3, and Llama 4 compare? A comprehensive 2026 reference covering Turkish LLM performance, training stages, hallucination control, and cost modeling.
We benchmarked GPT-5.5, Claude Opus 4.7 and Gemini 3.1 Pro on Turkish workloads end to end: TR-MMLU and TUMLU benchmark numbers, a 50-prompt real-world test across legal, finance, code, creative writing and Q&A, an A/B in a Turkish enterprise, TL-based cost analysis and a decision matrix for picking the right model for each Turkish task. 35+ references.
A 2026 snapshot of the Turkish open-source LLM ecosystem: Trendyol-LLM, Cosmos-Llama, KanarYa, Kumru AI, the TÜBİTAK BİLGEM domestic model, and the T3 AI Baykar defense model. Detailed decision guide covering MMLU-TR and TUMLU benchmarks, licensing, tokenization gap, VRAM requirements, self-hosting needs, and which model to pick for which use case.
The most current, detailed 2026 Turkish guide to adapting an LLM to your domain. Covers when fine-tuning is necessary, the math behind LoRA, 4-bit training with QLoRA, why DPO beats PPO, modern alternatives (ORPO/KTO/IPO), Turkish dataset sources, GPU/cloud cost modeling, production pipelines, 3 anonymized Turkish enterprise case studies, and KVKK-compliant training. For developers, MLOps engineers, and AI architects.
The most comprehensive 2026 Turkish LLM benchmark: MMLU-TR, Belebele-TR, TruthfulQA-TR, Turkish HumanEval, MGSM-TR, and hallucination tests. Score tables for GPT-5, Claude Opus 4.7, Gemini 3, Mistral Large 3, Llama 4, DeepSeek V3, Qwen 2.5, and local Turkish models (Cezeri, BERTurk, Trendyol-LLM), with use-case mapping and transparent methodology.