ERNIE-4.5-0.3B-Base
Parameters
300M
Context Length
131K
Modality
Text
Architecture
Dense
License
Apache License 2.0
Release Date
30 Jun 2025
Knowledge Cutoff
-
API Pricing (per 1M)
Self-hosted only
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
131,072 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for ERNIE-4.5-0.3B-Base available.
Rankings
Overall Rank
-
Coding Rank
-
About ERNIE-4.5-0.3B-Base
ERNIE-4.5-0.3B-Base is Baidu's open-source 360M parameter dense transformer base model designed for lightweight on-device pretraining and fine-tuning. It supports bilingual text generation across an extensive 131K token context window.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
16
Key-Value Heads
2
Attention Head Dimension
128
Position Embedding
Absolute Position Embedding
RoPE Theta
500,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
Swish
Dimensions
Hidden Dimension Size
1,024
Number of Layers
18
FFN Intermediate Size (Dense)
3,072
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
103,424
About ERNIE 4.5
The Baidu ERNIE 4.5 family consists of ten large-scale multimodal models. They utilize a heterogeneous Mixture-of-Experts (MoE) architecture, which enables parameter sharing across modalities while also employing dedicated parameters for specific modalities, supporting efficient language and multimodal processing.