Search Results for "stanford"

Run a fast ChatGPT-like model locally on your device. This combines the LLaMA foundation model with an open reproduction of Stanford Alpaca a fine-tuning of the base model to obey instructions (akin to the RLHF used to train ChatGPT) and a set of modifications to llama.cpp to add a chat interface. Download the zip file corresponding to your operating system from the latest release. The weights are based on the published fine-tunes from alpaca-lora, converted back into a PyTorch checkpoint...

This executable get a text file (input name "in.txt" at the same folder where the executable is) and creates a file called "out.txt" with the same content but each noun, adjective or verb is lemmatized. From the Aseryla (https://memla.000webhostapp.com/index.html) system that combines the Stanford Core NLP (https://stanfordnlp.github.io/CoreNLP/index.html) and the CSTlemmatiser(http://cst.dk/online/lemmatiser/uk/)

This is a relational database project based on RedBase project from Stanford University See https://web.stanford.edu/class/cs346/2015/redbase.html The program is implemented in C++.

Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java. At its core, it provides much the same functionality as the core of Moses. Distinctive features include: providing an easy to use API for implementing new decoding model features, the ability to translating using phrases that include gaps (Galley et al. 2010), and conditional extraction of phrase-tables and lexical reordering models. Developed by The Natural Language Processing Group...

The Whole-Body Control framework jointly developed at Stanford University and The University of Texas at Austin provides advanced control for fixed base manipulators and is currently running on the the Meka A2 Arm and the Dreamer/Meka Humanoid robot. The code repository is hosted on Github, please go to https://github.com/poftwaresatent/stanford_wbc

Software Infrastructure for Stanford's Autonomous Vehicles

S-GeMS is a software for 3D geostatistical modeling. It implements many of the classical geostatistics algorithms, as well as new developments made at Stanford University. See http://sgems.sourceforge.net

The STAIR Vision Library (SVL), originally developed to support the STanford AI Robot, provides software infrastructure for computer vision, machine learning, and probabilistic graphical models.

This is an implementation of the adaptive biasing force method (ABF) in NAMD. The code for ABF was developed by Eric Darve, Stanford University. ABF is a method created by E. Darve and A. Pohorille to calculate the free energy of bio-molecules.

The Stanford Robotics Toolkit. An extensive, plugin-based architecture supporting the simulation and implementation of robotics algorithms. Designed for research purposes, the framework features a processing environment, a 3D GUI, and a CARMEN interface.

The Freedows OS Project is creating an open source core Kernel architecture based on the Stanford Cache Kernel model, with the aim to create a stable platform for kernel-level multi-platform emulation.