This is the Linux app named DeepSeek MoE whose latest release can be downloaded as DeepSeek-MoEsourcecode.zip. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS:
DeepSeek MoE
DESCRIPTION:
DeepSeek-MoE (“DeepSeek MoE”) is the DeepSeek open implementation of a Mixture-of-Experts (MoE) model architecture meant to increase parameter efficiency by activating only a subset of “expert” submodules per input. The repository introduces fine-grained expert segmentation and shared expert isolation to improve specialization while controlling compute cost. For example, their MoE variant with 16.4B parameters claims comparable or better performance to standard dense models like DeepSeek 7B or LLaMA2 7B using about 40% of the total compute. The repo publishes both Base and Chat variants of the 16B MoE model (deepseek-moe-16b) and provides evaluation results across benchmarks. It also includes a quick start with inference instructions (using Hugging Face Transformers) and guidance on fine-tuning (DeepSpeed, hyperparameters, quantization). The licensing is MIT for code, with a “Model License” applied to the models.
Features
- Fine-grained segmentation and expert specialization architecture
- Base and Chat versions of 16B model released publicly
- Significant compute efficiency: ~40% of compute while matching dense models
- Hugging Face inference integration and quantization support
- DeepSpeed-based fine-tuning scripts and hyperparameter guidance
- MIT-licensed code, structured for research and downstream usage
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/deepseek-moe.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.