This is the Linux app named DeepSeek-V3 whose latest release can be downloaded as v1.0.0sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named DeepSeek-V3 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 the OnWorks Linux online or Windows online emulator or MACOS online emulator from this website.
- 5. From the OnWorks Linux 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, install it and run it.
SCREENSHOTS
Ad
DeepSeek-V3
DESCRIPTION
DeepSeek-V3 is a robust Mixture-of-Experts (MoE) language model developed by DeepSeek, featuring a total of 671 billion parameters, with 37 billion activated per token. It employs Multi-head Latent Attention (MLA) and the DeepSeekMoE architecture to enhance computational efficiency. The model introduces an auxiliary-loss-free load balancing strategy and a multi-token prediction training objective to boost performance. Trained on 14.8 trillion diverse, high-quality tokens, DeepSeek-V3 underwent supervised fine-tuning and reinforcement learning to fully realize its capabilities. Evaluations indicate that it outperforms other open-source models and rivals leading closed-source models, achieving this with a training duration of 55 days on 2,048 Nvidia H800 GPUs, costing approximately $5.58 million.
Features
- 671 billion parameters with 37 billion activated per token, ensuring robust language modeling.
- Multi-head Latent Attention (MLA) and DeepSeekMoE architecture for efficient computation.
- Auxiliary-loss-free load balancing strategy to enhance performance without additional losses.
- Multi-token prediction training objective for improved predictive capabilities.
- Pre-trained on 14.8 trillion diverse tokens, ensuring comprehensive language understanding.
- Supervised fine-tuning and reinforcement learning to fully harness model potential.
- Outperforms other open-source models, comparable to leading closed-source counterparts.
- Cost-effective training, completed in 55 days using 2,048 Nvidia H800 GPUs at approximately $5.58 million.
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/deepseek-v3.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
