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-Base

The ERNIE-4.5-0.3B-Base model is a constituent of Baidu's ERNIE 4.5 family of foundation models, explicitly engineered for general-purpose text understanding and generation tasks. This variant is characterized by its compact size, featuring 360 million parameters, and a dense architectural design, rendering it suitable for deployment in environments with limited computational resources or for applications requiring a lightweight inference footprint. As an open-source offering under the Apache License 2.0, it provides a foundational language model for developers and researchers to build upon and integrate into various text-centric systems.

From an architectural standpoint, ERNIE-4.5-0.3B-Base implements a transformer structure comprising 18 layers. It utilizes 16 attention heads for queries and 2 key-value heads, indicating a Grouped-Query Attention (GQA) mechanism for efficient processing. The model is trained to support a substantial context length of up to 131,072 tokens, enabling it to process and generate coherent text over extended sequences. Unlike some other variants within the ERNIE 4.5 series, this model employs a dense architecture rather than a Mixture-of-Experts (MoE) structure. The hidden dimension size is 1024, and it employs RMS Normalization and the Swish (SiLU) activation function. The model utilizes an absolute position embedding.

This model is primarily optimized for text completion and can be fine-tuned for specialized applications through various methods, including Supervised Fine-tuning (SFT), Low-Rank Adaptation (LoRA), and Direct Preference Optimization (DPO). Its compatibility with widely adopted frameworks such as Hugging Face Transformers and Baidu's FastDeploy toolkit facilitates its integration into existing development workflows. The model is designed to support both English and Chinese languages.

没有可用的 ERNIE-4.5-0.3B-Base 评估基准。