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| # AhıskaAI |
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| An independent, open-source AI research lab specializing in Small Language Models (SLMs), custom tokenizers, robust OCR architectures, and highly curated niche datasets. Built from the ground up with deep technical curiosity. |
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| ## 🧠 Our Approach: "Fail Forward" & Open Code |
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| We believe true machine learning engineering happens through transparency. Instead of only showing perfected weights, AhıskaAI documents the entire lifecycle of model development. |
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| Our workspace is organized into explicit tracks: |
| * **Base Models:** Architectures trained from scratch using our custom-built BPE tokenizers. |
| * **Fine-Tuned Models:** Production-ready SLMs optimized for specific context-driven tasks (translation, historical synthesis, and niche NLP). |
| * **Curated Datasets:** Cleaned, structured data pipelines (including synthetic optimization and conversational formatting). |
| * **Failed Models:** Our explicit log of failed training runs, gradient explosions, and alignment experiments. We publish our mistakes so the community can learn from them. |
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| ## 🛠️ Tech Stack & Focus |
| * **Architectures:** Custom Transformer SLMs (24M to 125M+ parameters) |
| * **NLP Pipelines:** Custom BPE Tokenization, Synthetically Enhanced Datasets (ShareGPT/Alpaca formats) |
| * **Computer Vision:** High-frequency OCR models trained for localized data extraction and captcha bypasses |
| * **Methodologies:** From-scratch Pre-training, Supervised Fine-Tuning (SFT) |
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| ## 🌍 Identity & Mission |
| Named in honor of the Ahıska Turks, our long-term roadmap focuses on bridging state-of-the-art deep learning with cultural and historical preservation—bringing heritage and accurate documentation into the open-source digital landscape. |
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| *Driven by passion. Powered by local compute.* |
