Update url.md

README.md

@@ -78,7 +78,7 @@ pip install mmengine==0.10.3
 pip install bitsandbytes-0.37.0+das1.0+gitd3d888f.abi0.dtk2404.torch2.1-py3-none-any.whl
 ```
 
-2. 通过[预训练权重](#预训练权重)下载预训练模型，当前用例使用[Meta-Llama-3-8B-Instruct](http://113.200.138.88:18080/aimodels/Meta-Llama-3-8B-Instruct)模型；
+2. 通过[预训练权重](#预训练权重)下载预训练模型，当前用例使用[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)模型；
 
 3. 修改[llama3_8b_instruct_qlora_alpaca_e3_M.py](./llama3_8b_instruct_qlora_alpaca_e3_M.py)代码中的`pretrained_model_name_or_path`、`data_path`为本地模型、数据地址；
 
@@ -100,7 +100,7 @@ NPROC_PER_NODE=${DCU_NUM} xtuner train ./llama3_8b_instruct_qlora_alpaca_e3_M.py
 git clone -b v0.6.3 http://developer.hpccube.com/codes/OpenDAS/llama-factory.git
 ```
 
-2. 通过[预训练权重](#预训练权重)下载预训练模型，当前用例使用[Meta-Llama-3-8B-Instruct](http://113.200.138.88:18080/aimodels/Meta-Llama-3-8B-Instruct)模型；
+2. 通过[预训练权重](#预训练权重)下载预训练模型，当前用例使用[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)模型；
 
 3. `llama3/3.1`训练脚本可参考[这里](./llama-factory/examples/)，特别地，选择`single_node.sh`启动的，需要确认`single_config.yaml`文件中`num_processes`参数与设置的显卡数量一致。
 
@@ -257,15 +257,15 @@ python eval.py --model hf --model_args pretrained=/home/llama3/Meta-Llama-3-8B-I
 制造,广媒,家居,教育
 
 ## 预训练权重
-通过[SCNet AIModels](http://113.200.138.88:18080/aimodels)下载预训练模型:
-- [Meta-Llama-3-8B](http://113.200.138.88:18080/aimodels/Meta-Llama-3-8B)
-- [Meta-Llama-3-8B-Instruct](http://113.200.138.88:18080/aimodels/Meta-Llama-3-8B-Instruct)
-- [Meta-Llama-3-70B](http://113.200.138.88:18080/aimodels/Meta-Llama-3-70B)
-- [Meta-Llama-3-70B-Instruct](http://113.200.138.88:18080/aimodels/Meta-Llama-3-70B-Instruct)
-- [Meta-Llama-3.1-8B](http://113.200.138.88:18080/aimodels/meta-llama/Meta-Llama-3.1-8B)
-- [Meta-Llama-3.1-8B-Instruct](http://113.200.138.88:18080/aimodels/Meta-Llama-3.1-8B-Instruct)
-- [Meta-Llama-3.1-70B](http://113.200.138.88:18080/aimodels/meta-llama/Meta-Llama-3.1-70B)
-- [Meta-Llama-3.1-70B-Instruct](http://113.200.138.88:18080/aimodels/meta-llama/Meta-Llama-3.1-70B-Instruct)
+通过HF下载预训练模型:
+- [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
+- [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+- [Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)
+- [Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)
+- [Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B)
+- [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
+- [Meta-Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B)
+- [Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)
 
 模型目录结构如下：
 ```bash