This is a PyTorch implementation of ConvTran
-
Updated
Mar 21, 2025 - Python
This is a PyTorch implementation of ConvTran
[ICLR 2026] GRAPE: Group Representational Position Encoding (https://arxiv.org/abs/2512.07805)
Official Project Page for HLA: Higher-order Linear Attention (https://arxiv.org/abs/2510.27258)
[ICLR'25] Data and code for our paper "Why Does the Effective Context Length of LLMs Fall Short?"
PyTorch implementation of RoFormer's Rotary Position Embeddings (RoPE) with comprehensive tests, benchmarks, and documentation. Used in LLaMA, PaLM, GPT-NeoX.
Position-Guided Transformer for Image Captioning. Image and Vision computing.
Trains a neural network to output the RGB color for a given XYZ coordinate on a vertex colored 3D mesh.
Benchmarking RoPE scaling methods on TinyLlama-1.1B to measure perplexity degradation when extending context from 2048 to 8192 tokens. NTK-aware scaling preserves quality almost perfectly while linear scaling collapses.
Add a description, image, and links to the position-encoding topic page so that developers can more easily learn about it.
To associate your repository with the position-encoding topic, visit your repo's landing page and select "manage topics."