

Skywork MoE Base
Overview :
Skywork-MoE-Base is a high-performance mixed expert (MoE) model with 146 billion parameters, comprising 16 experts and activating 22 billion parameters. The model is initialized from the dense checkpoint of the Skywork-13B model and introduces two innovative techniques: gated logical normalization enhances expert diversity, and an adaptive auxiliary loss coefficient allows for layer-specific adjustment of the auxiliary loss coefficient. Skywork-MoE exhibits comparable or superior performance to models with more parameters or activation parameters on various popular benchmark tests.
Target Users :
The Skywork-MoE-Base model is designed for developers and researchers who need to handle large-scale language model inference. Its high performance and innovative technology make it an ideal choice for complex text generation and analysis tasks.
Use Cases
Generate detailed descriptions of the capitals of Chinese provinces
Conduct multi-turn dialogue generation, such as asking consecutive questions about provincial capitals
Rapidly deploy for research and development of new language model applications
Features
A large-scale mixed expert model with 146 billion parameters
16 experts and 22 billion activated parameters
Introduces two innovative techniques: gated logical normalization and adaptive auxiliary loss coefficient
Outperforms on multiple benchmark tests
Supports Hugging Face model inference
Provides a fast deployment method based on vLLM
Supports local environments and Docker deployment
How to Use
Step 1: Install the necessary dependencies
Step 2: Clone the Skywork-provided vllm code repository
Step 3: Compile and install vllm
Step 4: Choose between local environment or Docker deployment as needed
Step 5: Set the model path and working directory
Step 6: Run the Skywork MoE model for text generation using vllm
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