🧠 Model Overview

  • This project provides an English-to-Chinese translation model based on the RWKV-V7 architecture, with approximately 0.4 billion parameters and 1.5 billion parameters.
  • Model weight fine-tuning base on https://huggingface.co/BlinkDL/rwkv7-g1
  • The model has been fully fine-tuned on translation tasks and demonstrates strong performance across various domains, especially in handling long sentences, technical terminology, and culturally nuanced expressions.
  • Unlike traditional Transformer-based models, RWKV combines the sequential state-passing mechanism of RNNs with the parallel training capabilities of Transformers. This unique design enables efficient inference while maintaining powerful sequence modeling abilities, making it ideal for deployment on resource-constrained environments such as mobile devices, embedded systems, or edge computing platforms.

πŸ“¦ Install Dependencies

🟒 For Nvidia CUDA

pip install torch rwkv gradio python-docx PyPDF2 chardet

πŸ”΄ For AMD ROCm

  • set os.environ["RWKV_CUDA_ON"] = '0'
pip install torch --index-url https://download.pytorch.org/whl/rocm6.3
pip install rwkv python-docx PyPDF2 chardet

😜 Run The demo

  • Change line 20 in webui_new.py to you own model weights path
python webui_new.py 

⚠️ Notice

- This model currently supports English β†’ Chinese translation only.

  • Now it support English β†’ Chinese & Chinese β†’ English ~~~

πŸ’‘ Key Advantages

  • βœ… Lightweight and Deployment-Friendly: Achieves high-quality translation with only 0.4B / 1.5B parameters.
  • βœ… Strong Long-Context Modeling: Supports input lengths up to 4096 tokens.
  • βœ… Low Memory Footprint: Ideal for edge devices, mobile apps, and embedded systems.
  • βœ… Multilingual Potential: Built upon a multilingual pre-training foundation, future versions may support more language pairs.

🎁 Recommended Resources

🧩 Developer Info

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