APUS-xDAN-4.0-MOE / README.md
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---
license: apache-2.0
---
![image/png](https://cdn-uploads.huggingface.co/production/uploads/643197ac288c9775673a01e9/w-lgOpASM1DMl2PO0kdFy.png)
## 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
## Usage
### Initial
```python
git clone https://github.com/ggerganov/llama.cpp.git
make LLAMA_CUDA=1
```
### Interactive Chat
```python
./main -m APUS-xDAN-4.0-quanzied_version.gguf \
--prompt "You are a helpful assistant named APUS-xDAN4.0 MoE." --chatml \
--interactive \
--temp 0.7 \
--ctx-size 4096 (32k)
```
License
APUS-xDAN-4.0-MOE is distributed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.