Update README with CUDA library setup instructions and correct NumPy version

README.md

@@ -25,7 +25,7 @@ Complete PyTorch ML stack with all dependencies - no conflicts, easy installatio
 - **Python:** 3.10 compatible
 - **PyTorch:** 2.7.1 + CUDA 12.6
 - **Transformers:** 4.52.3
-- **NumPy:** 1.26.4 (compatible version)
+- **NumPy:** 2.0.2 (compatible version)
 - **SciPy:** 1.15.2
 - **All Dependencies:** 80+ wheels, fully tested together
 
@@ -53,11 +53,24 @@ subprocess.run(["pip", "install"] + [f"{wheel_path}/*.whl"], shell=True)
 # 1. Download repository
 git clone https://huggingface.co/RDHub/pytorch_python_310
 
-# 2. Install everything
+# 2. Install everything with requirements file for correct versions
 cd pytorch_python_310
-pip install lib_wheel/*.whl
-
-# 3. Test (optional)
+pip install -r lib_wheel/requirements.txt --find-links lib_wheel --no-index
+
+# 3. Set up CUDA libraries (for conda environments)
+# Create activation script for automatic library path setup
+mkdir -p $CONDA_PREFIX/etc/conda/activate.d
+cat > $CONDA_PREFIX/etc/conda/activate.d/pytorch_cuda_libs.sh << 'EOF'
+#!/bin/bash
+# Set up NVIDIA CUDA library paths for PyTorch
+NVIDIA_LIB_PATH=$(find $CONDA_PREFIX -path "*/nvidia/*/lib" -type d 2>/dev/null | tr '\n' ':')
+CUSPARSELT_LIB_PATH=$(find $CONDA_PREFIX -path "*/cusparselt/lib" -type d 2>/dev/null | tr '\n' ':')
+export LD_LIBRARY_PATH="${NVIDIA_LIB_PATH}${CUSPARSELT_LIB_PATH}${LD_LIBRARY_PATH}"
+EOF
+chmod +x $CONDA_PREFIX/etc/conda/activate.d/pytorch_cuda_libs.sh
+
+# 4. Reactivate environment and test
+conda deactivate && conda activate your_env_name
 python -c "import torch; print(f'PyTorch {torch.__version__} - CUDA: {torch.cuda.is_available()}')"
 ```
 
@@ -67,7 +80,7 @@ python -c "import torch; print(f'PyTorch {torch.__version__} - CUDA: {torch.cuda
 |---------|---------|---------|
 | PyTorch | 2.7.1 | 3.10 |
 | Transformers | 4.52.3 | 3.10 |
-| NumPy | 1.26.4 | 3.10 |
+| NumPy | 2.0.2 | 3.10 |
 | CUDA | 12.6 | - |
 
 ## 🎯 Use Cases
@@ -83,8 +96,9 @@ Perfect for:
 
 - **No dependency conflicts** - all versions tested together
 - **Offline ready** - no internet needed after download
-- **CUDA included** - ready for GPU training
+- **CUDA included** - ready for GPU training with library path setup
 - **Linux x86_64** compatible
+- **Requires conda environment** - for automatic CUDA library path management
 
 ---