技术规格

系统要求

不同量化方法和上下文大小的显存要求

ERNIE-4.5-300B-A47B

ERNIE-4.5-300B-A47B is a foundational language model within Baidu's ERNIE 4.5 family, designed to support advanced natural language processing tasks. While the broader ERNIE 4.5 series encompasses multimodal capabilities, this specific variant focuses on text-only applications, optimizing its architecture for efficient and robust language understanding and generation. Its primary purpose is to serve as a high-performance solution for general-purpose textual analysis and creation, including complex reasoning and knowledge-intensive tasks. The model supports text generation in both English and Chinese.

The model's technical foundation is a Mixture-of-Experts (MoE) architecture, featuring a total of 300 billion parameters with 47 billion parameters actively engaged per token during inference. The overarching ERNIE 4.5 MoE design includes a novel heterogeneous structure that facilitates parameter sharing while also allowing for dedicated parameters across different modalities, optimizing for multimodal understanding without compromising text-related performance. Key architectural enhancements include concepts like Dynamic Attention Masking (FlashMask), which contributes to efficient information processing, and modality-isolated routing. The model is trained using Baidu's PaddlePaddle deep learning framework, employing advanced techniques such as intra-node expert parallelism, memory-efficient pipeline scheduling, and FP8 mixed-precision training to achieve high throughput during pre-training.

For deployment and operational efficiency, ERNIE-4.5-300B-A47B supports highly efficient inference through methods like multi-expert parallel collaboration and convolutional code quantization, enabling near-lossless 4-bit and 2-bit quantization for diverse hardware configurations. It maintains a substantial context length of 131,072 tokens, allowing for the processing of extensive textual inputs and enabling coherent, long-form content generation. The model is also designed to be fine-tuned and deployed with developer toolkits like ERNIEKit and FastDeploy, making it accessible for a range of commercial and research applications under the Apache 2.0 license.

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