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-4.5-0.3B

The ERNIE-4.5-0.3B model represents a foundational component within Baidu's ERNIE 4.5 model family. This model is designed as a compact and efficient language model, focusing on general-purpose natural language understanding and generation tasks. Unlike the larger Mixture-of-Experts (MoE) variants in the ERNIE 4.5 series, ERNIE-4.5-0.3B employs a dense transformer architecture, making it suitable for high-throughput applications and resource-constrained environments. Its engineering prioritizes robust language capabilities while maintaining a minimal operational footprint, enabling deployment across diverse scenarios where computational efficiency is paramount.

Architecturally, ERNIE-4.5-0.3B is built upon a dense transformer framework, featuring 18 layers and 16 attention heads. This configuration supports its text processing capabilities, facilitating the interpretation and generation of textual content. The model benefits from training methodologies shared across the broader ERNIE 4.5 family, which incorporate advanced techniques for optimized efficiency. While larger, multimodal ERNIE 4.5 models integrate heterogeneous Mixture-of-Experts and specific multimodal training, ERNIE-4.5-0.3B maintains its focus on delivering high performance within its text-only, dense design.

Designed for practical application, ERNIE-4.5-0.3B is particularly well-suited for high-throughput and edge computing deployments. Its use cases encompass tasks such as sentiment analysis, topic categorization, and spam detection at scale. The model's optimized design further enables direct execution on mobile devices, supporting real-time applications such as text completion or streamlined question-answering systems. Additionally, ERNIE-4.5-0.3B is capable of generating concise summaries of text documents, providing an efficient tool for rapid information review.

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