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license: apache-2.0 |
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## Introduction |
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APUS-xDAN-4.0-MOE is a transformer-based decoder-only language model, developed on a vast corpus of data to ensure robust performance. |
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This is an enhanced MoE (Mixture of Experts) model built on top of the continued pre-training enhanced LlaMA architecture, |
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further optimized with human-enhanced feedback algorithms to improve reasoning, mathematical, and logical capabilities during inference. |
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For more comprehensive information, please visit our blog post and GitHub repository. |
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https://github.com/shootime2021/APUS-xDAN-4.0-moe |
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# Model Details |
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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. |
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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. |
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The following specifications: |
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- **Parameters:** 136B |
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- **Architecture:** Mixture of 4 Experts (MoE) |
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- **Experts Utilization:** 2 experts used per token |
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- **Layers:** 60 |
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- **Attention Heads:** 56 for queries, 8 for keys/values |
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- **Embedding Size:** 7,168 |
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- **Additional Features:** |
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- Rotary embeddings (RoPE) |
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- Supports activation sharding and 1.5bit~4bit quantization |
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- **Maximum Sequence Length (context):** 32,768 tokens |
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## Usage |
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### Initial |
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```python |
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git clone https://github.com/ggerganov/llama.cpp.git |
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make LLAMA_CUDA=1 |
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``` |
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### Interactive Chat |
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```python |
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./main -m APUS-xDAN-4.0-quanzied_version.gguf \ |
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--prompt "You are a helpful assistant." --chatml \ |
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--interactive \ |
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--temp 0.7 \ |
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--ctx-size 4096 |
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``` |
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License |
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APUS-xDAN-4.0-MOE is distributed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. |
