fix readme (#746)

README.md

@@ -39,7 +39,7 @@ To facilitate use by users unfamiliar with deep learning, we provide a Gradio we
 Additionally, we are expanding capabilities for other modalities. Currently, we support full-parameter training and LoRA training for AnimateDiff.
 
 ## 🎉 News
-- 2024.04.19: Support for single-card, DDP, ZeRO2, and ZeRO3 training and inference with NPU, please refer to [NPU Inference and Fine-tuning Best Practices](docs/source/LLM/NPU Inference and Fine-tuning Best Practices.md).
+- 2024.04.19: Support for single-card, DDP, ZeRO2, and ZeRO3 training and inference with NPU, please refer to [NPU Inference and Fine-tuning Best Practices](docs/source_en/LLM/NPU-best-practice.md).
 - 2024.04.19: Support for inference, fine-tuning, and deployment of **Llama3** series models. This includes: Llama-3-8B, Llama-3-8B-Instruct, Llama-3-70B, and Llama-3-70B-Instruct. use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/llama3_8b_instruct/lora/sft.sh) to train.
 - 2024.04.18: Supported models: wizardlm2-7b-awq, wizardlm2-8x22b, yi-6b-chat-awq, yi-6b-chat-int8, yi-34b-chat-awq, yi-34b-chat-int8. Supported `--deepspeed zero3-offload` and provided default zero3-offload configuration file for zero3+cpu offload usage.
 - 2024.04.18: Supported compatibility with HuggingFace ecosystem using the environment variable `USE_HF`, switching to use models and datasets from HF. Please refer to the [HuggingFace ecosystem compatibility documentation](https://github.com/modelscope/swift/tree/main/docs/source_en/LLM/Compat-HF.md).