Catha edulis
PulseAugur coverage of Catha edulis — every cluster mentioning Catha edulis across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
-
New Transformer Model Enhances Pedestrian Crossing Prediction for Autonomous Driving
Researchers have developed ADAPT (Adaptive Domain-Aware Pedestrian Crossing Transformer), a new multimodal framework designed to improve the prediction of pedestrian crossing intentions for autonomous driving. This syst…
-
AI inference tech aims to reduce disk spillover performance hit
New inference acceleration techniques like dSpark, dflash, MTP, and QAT are being explored to mitigate performance degradation when large language models spill over from RAM to disk. The core question is whether these a…
-
New MMPM framework improves pedestrian trajectory prediction from video
Researchers have developed a new framework called MMPM to improve pedestrian trajectory prediction from ego-centric videos. This model addresses the challenge of multimodal pedestrian behavior by separately modeling dis…
-
LLaMA user seeks advice on Gemma 4 31B quantizations and hardware optimization
A user on the r/LocalLLaMA subreddit is seeking advice on optimizing their setup for running large language models, specifically the Gemma 4 31B model. They are trying to determine if newer 'QAT' (Quantized Aware Traini…
-
Google Gemma 4 12B performance boosted by quantization techniques
A blog post compares the performance of the Google Gemma 4 12B model with and without quantization techniques, specifically MTP (Mixed Precision Training) and QAT (Quantization-Aware Training). The author provides speed…
-
Anthropic's Mythos AI to Transform Cybersecurity; Google Optimizes Gemma 4 for Local Use
Anthropic has reportedly developed a new AI model named "Mythos," which is expected to significantly impact cybersecurity defenses. Meanwhile, Google has introduced a memory-saving technique called QAT for its Gemma 4 m…
-
llama.cpp integrates Gemma 4 MTP for faster local model performance
The llama.cpp project has merged support for Gemma 4 MTP, a feature that enhances the speed and efficiency of local large language models. This integration allows users to leverage Gemma 4 with Quantization Aware Traini…
-
New MUSCLE-NET model improves pedestrian trajectory forecasting
Researchers have developed MUSCLE-NET, a novel network designed for predicting pedestrian trajectories in autonomous driving and transportation systems. This new model addresses limitations in existing methods by better…
-
Swin Transformer shows resilience to FP4 quantization in anomaly segmentation
A new research paper explores how model architecture, scale, and specific quantization-aware training (QAT) recipes affect the quality of anomaly segmentation models when using FP4 precision. The study found that attent…
-
Gemma 4 QAT models spark debate over performance and utility
Users are discussing the performance and utility of Gemma 4 QAT (Quantization Aware Training) models, particularly comparing them to standard quantizations. While some users report improved speed and quality for general…