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+## How to add tests
+
+To create a new PyTorch test, you can follow the below instruction.
+
+Assuming that the new PyTorch test have project name call `demo`, let's create the test skeleton:
+
+```bash
+cd tests/pytorch
+mkdir -p demo
+cd demo
+touch demo.c demo.py config.nix
+```
+
+Developers should put their PyTorch implementation into "<project-name>.py" file.
+For each PyTorch tests, developers must write the MLIR model to "forward.mlir" file.
+
+```python
+# demo.py
+#...
+with open("forward.mlir", "w") as mlir_module:
+    print(graph._imported_module, file = mlir_module)
+```
+
+For each PyTorch tests, developers should call the MLIR model from "<project-name>.c" file.
+In our case, here is an example "demo.c" file:
+
+```c
+// 1. Include the MemRef wrapper
+#include "memref.h"
+
+// 2. Create corresponding MemRef struct with data type `float` and one dimension.
+NEW_MEMREF(float, 1);
+
+// 3. Declare the MLIR model interface
+extern void _mlir_ciface_forward(struct MemRef_float_dim1 *output,
+                                 struct MemRef_float_dim1 *arg1,
+                                 struct MemRef_float_dim1 *arg2);
+
+// 4. Create example data array. The ".vdata" attribute will help emulator load the data into correct memory.
+__attribute((section(".vdata"))) float input_float_0[512] = {1, 2, 3};
+struct MemRef_float_dim1 input1 = {
+    .allocatedPtr = input_float_0,
+    .alignedPtr = input_float_0,
+    .offset = 0,
+    .sizes = {512},
+    .strides = {1},
+};
+
+// 5. Declare the main entry. In t1 all tests entry should be `int test()` instead of main().
+int test() {
+  _mlir_ciface_forward(&output, &input1, &input2);
+  return 0;
+}
+```
+
+After PyTorch model and the C entry is correctly created, developers should declare a "config.nix"
+file to indicate our build system to find and build the test case:
+
+```nix
+{
+  # Tell our build system to include the memref.h header.
+  # Developer could add extra headers here.
+  includes = [
+    ../memref.h
+  ];
+
+  # Tell the build system to run buddy-opt with three phrase, with arguments to run in each phrase
+  buddyOptArgs = [
+    [
+      "--pass-pipeline"
+      "builtin.module(func.func(tosa-to-linalg-named, tosa-to-linalg, tosa-to-tensor, tosa-to-arith), empty-tensor-to-alloc-tensor, convert-elementwise-to-linalg, arith-bufferize, func.func(linalg-bufferize, tensor-bufferize), func-bufferize)"
+    ]
+    [
+      "--pass-pipeline"
+      "builtin.module(func.func(buffer-deallocation-simplification, convert-linalg-to-loops), eliminate-empty-tensors, func.func(llvm-request-c-wrappers))"
+    ]
+    [
+      "--lower-affine"
+      "--convert-math-to-llvm"
+      "--convert-math-to-libm"
+      "--convert-scf-to-cf"
+      "--convert-arith-to-llvm"
+      "--expand-strided-metadata"
+      "--finalize-memref-to-llvm"
+      "--lower-vector-exp"
+      "--lower-rvv=rv32"
+      "--convert-vector-to-llvm"
+      "--convert-func-to-llvm"
+      "--reconcile-unrealized-casts"
+    ]
+  ];
+}
+```
+
+Our build system accept the below data layout for the "config.nix" file:
+
+```text
+Set {
+    buddyOptArgs: Array<Array<String>>,
+
+    includes: Optional<Array<String>>,
+    pythonArgs: Optional<Array<String>>,
+    buddyTranslateArgs: Optional<Array<String>>,
+    buddyLLCArgs: Optional<Array<String>>,
+}
+```
+
+After the project have been implemented, developers can run the below commands to build and test the ELF:
+
+```bash
+git add .
+nix build '.#t1.blastoise.ip.cases.pytorch.demo' -L
+ls ./result/bin/pytorch-demo.elf
+
+# To start the emulator and get waveform, run:
+nix build '.#t1.blastoise.ip.cases.pytorch.demo.emu-result.with-trace' -L
+```
+
+## FAQ
+
+* How to debug the PyTorch code
+
+```bash
+nix run '.#buddy-mlir-pyenv' -- demo.py
+```
+
+* How to run buddy compiler tools manually
+
+```bash
+nix develop '.#t1.blastoise.ip.cases.pytorch.demo' -L
+cd $(mktemp -d -t 'pytorch-debug-XXX')
+pwd
+
+# Unpack sources
+unpackPhase
+# Check commands:
+echo -e "$buildPhase"
+# Run build
+bash -c "$buildPhase"
+```