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-VL-424B-A47B

ERNIE 4.5 is a large-scale multimodal foundation model family developed by Baidu, designed to integrate and process information across both textual and visual modalities. The ERNIE-4.5-VL-424B-A47B variant is specifically engineered for advanced comprehension and generation capabilities, supporting applications that demand complex understanding and creative output from diverse data types. These applications include sophisticated conversational AI, multimodal content creation, and intelligent analysis systems, all aiming to provide high performance across a wide spectrum of tasks.

This model variant employs a heterogeneous Mixture of Experts (MoE) architecture, comprising 424 billion total parameters with 47 billion activated parameters per token. A key architectural innovation is its novel design that supports parameter sharing across modalities while also allowing for dedicated expert parameters for each individual modality. This structure enhances multimodal understanding without compromising performance on text-related tasks. The model features 54 layers and utilizes Grouped Query Attention (GQA) with 64 attention heads and 8 key-value heads. Its positional encoding strategy integrates multimodal positional embeddings for unified hidden states and incorporates 2D rotary position embedding (RoPE) within the vision encoder. The system routes text and vision features to distinct sets of experts while simultaneously using shared experts and self-attention layers for all tokens, thereby facilitating cross-modal knowledge integration. The architecture includes 64 distinct text experts and 64 distinct vision experts, with 8 active experts selected for each modality per token. Furthermore, it incorporates modality-isolated routing, router orthogonal loss, and multimodal token-balanced loss to optimize training and prevent interference between modalities.

ERNIE-4.5-VL-424B-A47B is engineered for tasks requiring cross-modal comprehension and generation, supporting a context length of 131,072 tokens. Its substantial parameter base and efficient MoE design enable the model to process extensive and complex inputs, fostering deep semantic understanding and coherent long-form generation across both text and images. The model offers distinct "thinking" and "non-thinking" modes to accommodate varied reasoning approaches. Potential use cases encompass multimodal content generation, advanced dialogue systems, comprehensive visual question answering, document and chart understanding, and general multimodal analysis where the synthesis of different data types is critical. For enhanced inference efficiency, the model supports deployment with quantization, including 4-bit and 2-bit lossless quantization. The entire ERNIE 4.5 family, including this variant, is built on the PaddlePaddle deep learning framework, which contributes to its high-performance inference and streamlined deployment capabilities.

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