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+
+# ExecuTorch
+
+[ExecuTorch](https://docs.pytorch.org/executorch/stable/index.html) is a lightweight runtime for model inference on edge devices. It exports a PyTorch model into a portable, ahead-of-time format. A small C++ runtime plans memory and dispatches operations to hardware-specific backends. Execution and memory behavior is known before the model runs on device, so inference overhead is low.
+
+Export a Transformers model with the [optimum-executorch](https://huggingface.co/docs/optimum-executorch/en/index) library.
+
+<hfoptions id="export">
+<hfoption id="CLI">
+
+```bash
+optimum-cli export executorch \
+    --model "HuggingFaceTB/SmolLM2-135M-Instruct" \
+    --task "text-generation" \
+    --recipe "xnnpack" \
+    --output_dir="./smollm2_exported"
+```
+
+</hfoption>
+<hfoption id="Python">
+
+```py
+from transformers import AutoTokenizer
+from optimum.executorch import ExecuTorchModelForCausalLM
+
+model = ExecuTorchModelForCausalLM.from_pretrained(
+    "HuggingFaceTB/SmolLM2-135M-Instruct",
+    recipe="xnnpack",
+)
+model.save_pretrained("./smollm2_exported")
+tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M-Instruct")
+```
+
+</hfoption>
+</hfoptions>
+
+## Transformers integration
+
+The export process uses several Transformers components.
+
+1. [`~PreTrainedModel.from_pretrained`] loads the model weights in safetensors format.
+2. Optimum applies graph optimizations and runs [torch.export](https://docs.pytorch.org/docs/stable/export.html) to create a `model.pte` file targeting your hardware backend.
+3. [`AutoTokenizer`] or [`AutoProcessor`] loads the tokenizer or processor files and runs during inference.
+4. At runtime, a C++ runner class executes the `.pte` file on the ExecuTorch runtime.
+
+```c++
+#include <executorch/extension/llm/runner/text_llm_runner.h>
+
+using namespace executorch::extension::llm;
+
+int main() {
+  // Load tokenizer and create runner
+  auto tokenizer = load_tokenizer("path/to/tokenizer.json", nullptr, std::nullopt, 0, 0);
+  auto runner = create_text_llm_runner("path/to/model.pte", std::move(tokenizer));
+
+  // Load the model
+  runner->load();
+
+  // Configure generation
+  GenerationConfig config;
+  config.max_new_tokens = 100;
+  config.temperature = 0.8f;
+
+  // Generate text with streaming output
+  runner->generate("The capital of France is", config,
+    [](const std::string& token) { std::cout << token << std::flush; },
+    nullptr);
+
+  return 0;
+}
+```
+
+## Resources
+
+- [ExecuTorch](https://docs.pytorch.org/executorch/stable/index.html) docs
+- [torch.export](https://docs.pytorch.org/docs/stable/export.html) docs
+- [Exporting to production](../serialization#executorch) guide