This is the Windows app named CTranslate2 whose latest release can be downloaded as CTranslate23.20.0.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named CTranslate2 with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS
Ad
CTranslate2
DESCRIPTION
CTranslate2 is a C++ and Python library for efficient inference with Transformer models. The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU. The execution is significantly faster and requires less resources than general-purpose deep learning frameworks on supported models and tasks thanks to many advanced optimizations: layer fusion, padding removal, batch reordering, in-place operations, caching mechanism, etc. The model serialization and computation support weights with reduced precision: 16-bit floating points (FP16), 16-bit integers (INT16), and 8-bit integers (INT8). The project supports x86-64 and AArch64/ARM64 processors and integrates multiple backends that are optimized for these platforms: Intel MKL, oneDNN, OpenBLAS, Ruy, and Apple Accelerate.
Features
- Encoder-decoder models supported
- GPT-2, GPT-J, GPT-NeoX, OPT, BLOOM supported
- Automatic CPU detection and code dispatch
- Fast and efficient execution on CPU and GPU
- Quantization and reduced precision
- Multiple CPU architectures support
- Dynamic memory usage
- Parallel and asynchronous execution
Programming Language
C++
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/ctranslate2.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.