Model Detail
Llama-3.1-8B-Instruct
▲ 3.3%Does Tone Change the Answer? Evaluating Prompt Politeness Effects on Modern LLMs: GPT, Gemini, and LLaMA
arXiv:2512.12812v2 Announce Type: replace-cross Abstract: Prompt engineering has emerged as a critical factor influencing large language model (LLM) performance, yet the impact of pragmatic elements such as linguistic tone and politeness remains underexplored, particularly across different model fam
GGML and llama.cpp join HF to ensure the long-term progress of Local AI
New in llama.cpp: Model Management
Measuring Open-Source Llama Nemotron Models on DeepResearch Bench
SAGE: Sign-Adaptive Gradient for Memory-Efficient LLM Optimization
arXiv:2604.07663v1 Announce Type: new Abstract: The AdamW optimizer, while standard for LLM pretraining, is a critical memory bottleneck, consuming optimizer states equivalent to twice the model's size. Although light-state optimizers like SinkGD attempt to address this issue, we identify the embedd
The Last Fingerprint: How Markdown Training Shapes LLM Prose
arXiv:2603.27006v1 Announce Type: cross Abstract: Large language models produce em dashes at varying rates, and the observation that some models "overuse" them has become one of the most widely discussed markers of AI-generated text. Yet no mechanistic account of this pattern exists, and the paralle
