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Paper#NLP, Language Modeling, Turkish Language🔬 ResearchAnalyzed: Jan 3, 2026 16:15

TabiBERT: A Modern BERT for Turkish NLP

Published:Dec 28, 2025 20:18
1 min read
ArXiv

Analysis

This paper introduces TabiBERT, a new large language model for Turkish, built on the ModernBERT architecture. It addresses the lack of a modern, from-scratch trained Turkish encoder. The paper's significance lies in its contribution to Turkish NLP by providing a high-performing, efficient, and long-context model. The introduction of TabiBench, a unified benchmarking framework, further enhances the paper's impact by providing a standardized evaluation platform for future research.
Reference

“TabiBERT attains 77.58 on TabiBench, outperforming BERTurk by 1.62 points and establishing state-of-the-art on five of eight categories.”

PermalinkArXiv
Research#llm🏛️ OfficialAnalyzed: Dec 27, 2025 20:00

I figured out why ChatGPT uses 3GB of RAM and lags so bad. Built a fix.

Published:Dec 27, 2025 19:42
1 min read
r/OpenAI

Analysis

This article, sourced from Reddit's OpenAI community, details a user's investigation into ChatGPT's performance issues on the web. The user identifies a memory leak caused by React's handling of conversation history, leading to excessive DOM nodes and high RAM usage. While the official web app struggles, the iOS app performs well due to its native Swift implementation and proper memory management. The user's solution involves building a lightweight client that directly interacts with OpenAI's API, bypassing the bloated React app and significantly reducing memory consumption. This highlights the importance of efficient memory management in web applications, especially when dealing with large amounts of data.
Reference

“React keeps all conversation state in the JavaScript heap. When you scroll, it creates new DOM nodes but never properly garbage collects the old state. Classic memory leak.”

Permalinkr/OpenAI
Research#llm📝 BlogAnalyzed: Dec 25, 2025 13:55

BitNet b1.58 and the Mechanism of KV Cache Quantization

Published:Dec 25, 2025 13:50
1 min read
Qiita LLM

Analysis

This article discusses the advancements in LLM lightweighting techniques, focusing on the shift from 16-bit to 8-bit and 4-bit representations, and the emerging interest in 1-bit approaches. It highlights BitNet b1.58, a technology that aims to revolutionize matrix operations, and techniques for reducing memory consumption beyond just weight optimization, specifically KV cache quantization. The article suggests a move towards more efficient and less resource-intensive LLMs, which is crucial for deploying these models on resource-constrained devices. Understanding these techniques is essential for researchers and practitioners in the field of LLMs.
Reference

“LLM lightweighting technology has evolved from the traditional 16bit to 8bit, 4bit, but now there is even more challenge to the 1bit area and technology to suppress memory consumption other than weight is attracting attention.”

PermalinkQiita LLM