Introduction APUS-xDAN-4.0-MOE is a transformer-based decoder-only language model, developed on a vast corpus of data to ensure robust performance.
This is an enhanced MoE (Mixture of Experts) model built on top of the continued pre-training enhanced LlaMA architecture, further optimized with human-enhanced feedback algorithms to improve reasoning, mathematical, and logical capabilities during inference.
For more comprehensive information, please visit our blog post and GitHub repository. https://github.com/shootime2021/APUS-xDAN-4.0-moe
Model Details APUS-xDAN-4.0-MOE leverages the innovative Mixture of Experts (MoE) architecture, incorporating components from dense language models. Specifically, it inherits its capabilities from the highly performant xDAN-L2 Series. With a total of 136 billion parameters, of which 30 billion are activated during runtime, APUS-xDAN-4.0-MOE demonstrates unparalleled efficiency. Through advanced quantization techniques, our open-source version occupies a mere 42GB, making it seamlessly compatible with consumer-grade GPUs like the 4090 and 3090. The following specifications:
Parameters: 136B Architecture: Mixture of 4 Experts (MoE) Experts Utilization: 2 experts used per token Layers: 60 Attention Heads: 56 for queries, 8 for keys/values Embedding Size: 7,168 Additional Features: Rotary embeddings (RoPE) Supports activation sharding and 1.5bit~4bit quantization Maximum Sequence Length (context): 32,768 tokens