diff --git a/PyTorch/built-in/others/OpenFold_for_PyTorch/README.md b/PyTorch/built-in/others/OpenFold_for_PyTorch/README.md
index 8664ccd3dd91fecffe74cb17fb9ae68dc1025c7f..845521cae7e27af1216023c810783d4cf577b1f4 100644
--- a/PyTorch/built-in/others/OpenFold_for_PyTorch/README.md
+++ b/PyTorch/built-in/others/OpenFold_for_PyTorch/README.md
@@ -49,12 +49,6 @@
   
 - 安装依赖。
 
-  创建python3.9的conda并激活
-  ```
-  conda create -n openfold_env python=3.9
-  conda activate openfold_env
-  ```
-
   在模型源码包根目录下执行命令，安装模型对应PyTorch版本需要的依赖。
   ```
   pip install -r 2.1_requirements.txt  # PyTorch2.1版本
@@ -67,9 +61,15 @@
   pip install git+https://github.com/NVIDIA/dllogger.git
   pip install torch==2.1.0
 
-  conda install -c bioconda -c conda-forge hhsuite
-  conda install -c bioconda kalign2
-  conda install -c bioconda -c conda-forge mmseqs2
+  wget https://github.com/soedinglab/hh-suite/releases/download/v3.3.0/hhsuite-3.3.0-AVX2-Linux.tar.gz
+  tar xvfz hhsuite-3.3.0-AVX2-Linux.tar.gz
+  export PATH="$(pwd)/bin:$(pwd)/scripts:$PATH"
+
+  pip install git+https://github.com/TimoLassmann/kalign.git
+
+  wget https://mmseqs.com/latest/mmseqs-linux-avx2.tar.gz
+  tar xvfz mmseqs-linux-avx2.tar.gz
+  export PATH=$(pwd)/mmseqs/bin/:$PATH
   ```
 
 - 构建安装openfold。
diff --git a/PyTorch/built-in/others/OpenFold_for_PyTorch/scripts/install_third_party_dependencies.sh b/PyTorch/built-in/others/OpenFold_for_PyTorch/scripts/install_third_party_dependencies.sh
index 4d142a8192a71c436f54647e055215821906c6e3..4d79849ad156639991ad724361280ed5c98c9eed 100644
--- a/PyTorch/built-in/others/OpenFold_for_PyTorch/scripts/install_third_party_dependencies.sh
+++ b/PyTorch/built-in/others/OpenFold_for_PyTorch/scripts/install_third_party_dependencies.sh
@@ -12,7 +12,4 @@ ln -rs openfold/resources/stereo_chemical_props.txt tests/test_data/alphafold/co
 gunzip -c tests/test_data/sample_feats.pickle.gz > tests/test_data/sample_feats.pickle
 
 # This setting is used to fix a worker assignment issue during data loading
-conda env config vars set KMP_AFFINITY=none
-
-export LIBRARY_PATH=$CONDA_PREFIX/lib:$LIBRARY_PATH
-export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH
+export KMP_AFFINITY=none
diff --git a/PyTorch/built-in/others/OpenFold_for_PyTorch/test/val_openfold_8p.sh b/PyTorch/built-in/others/OpenFold_for_PyTorch/test/val_openfold_8p.sh
index 8f352d8b82b1c01a6dc4e45f6b454eb5381a6483..750c8598e21dde0db4034d6c6889b650ddff0f0c 100644
--- a/PyTorch/built-in/others/OpenFold_for_PyTorch/test/val_openfold_8p.sh
+++ b/PyTorch/built-in/others/OpenFold_for_PyTorch/test/val_openfold_8p.sh
@@ -45,7 +45,7 @@ python3 train_openfold.py  $data_path/pdb_data/mmcif_files \
 wait
 
 # 提取最后一行中的 val/loss 小数值并赋值给变量
-val_loss=$(tac openfold_val_8p.log | grep -m1 'val/loss' | sed -n 's/.*val\/loss[[:space:]]\+\([0-9.]\+\).*/\1/p')
+val_loss=$(grep 'val/loss' openfold_val_8p.log | tail -n1 | awk '{print $(NF-1)}')
 
 # 输出结果
 echo "val/loss: $val_loss"
\ No newline at end of file