DownloadERNIE-4.5-0.3B-Paddle is a compact 360 million parameter dense transformer model, post-trained for efficient general-purpose text generation in English and Chinese. Developed by Baidu as part of the ERNIE 4.5 series, it is designed for lightweight applications while maintaining strong language modeling capabilities. The model comprises 18 layers, 16 attention heads, and supports an extended context length of up to 131,072 tokens. It is optimized specifically for PaddlePaddle and integrates seamlessly with the ERNIEKit toolkit for training methods such as SFT, DPO, and LoRA. Inference can be rapidly deployed using FastDeploy or via the Transformers library with remote code trust enabled. Though compact, the model inherits architecture-level optimizations from larger ERNIE models, including efficient memory use and inference strategies. This model is well-suited for users working in the PaddlePaddle ecosystem who require a performant and accessible LLM for scalable tasks.
Features
- 360M parameter dense architecture (no MoE)
- Post-trained for improved downstream performance
- 18 layers and 16 attention heads
- 131,072 token context length
- PaddlePaddle native with ERNIEKit support
- Compatible with FastDeploy and Hugging Face Transformers
- Supports SFT, DPO, and LoRA fine-tuning
- Apache 2.0 license for commercial use
Project Samples
