Add QNN EP documentation to OGA including Genie note

docs/qnn.md

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+# QNN Execution Provider
+
+ONNX Runtime GenAI supports running models on Qualcomm Snapdragon NPUs via the QNN Execution
+Provider (EP). This enables hardware-accelerated LLM inference on Snapdragon-based devices on
+Windows ARM64 and Linux ARM64.
+
+For best performance, use the newer QAIRT-targeting pipelines from
+[olive-recipes](https://github.com/microsoft/olive-recipes), which optimize models for
+accelerated NPU inference via the Genie runtime. Older QNN-targeting recipes produce
+standard QNN models that also work but do not use this acceleration pathway.
+
+## Prerequisites
+
+**Packages**
+
+- `onnxruntime-genai` — the OGA runtime
+- `onnxruntime-qnn` — the QNN EP plugin ([PyPI](https://pypi.org/project/onnxruntime-qnn/) /
+  [NuGet](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.QNN))
+
+**Model**
+
+QNN inference requires a model prepared for the QNN EP, packaged as an ONNX file containing
+compiled QNN graph artifacts. The `genai_config.json` and model files are produced by the
+[olive-recipes](https://github.com/microsoft/olive-recipes) pipeline targeting the QNN backend.
+
+## Usage
+
+The QNN EP is a plugin and must be registered before loading the model. The `onnxruntime-qnn`
+package provides helpers to locate the library and EP name:
+
+```python
+import onnxruntime_genai as og
+import onnxruntime_qnn
+
+# Register the QNN EP plugin before loading the model.
+og.register_execution_provider_library(
+    onnxruntime_qnn.get_ep_name(),      # "QNNExecutionProvider"
+    onnxruntime_qnn.get_library_path()  # platform-aware path to the QNN EP shared library
+)
+
+# Load model. The genai_config.json from the olive-recipes pipeline already
+# specifies the QNN provider, so no further config is needed in the common case.
+# To override options, use og.Config directly:
+#   config = og.Config("/path/to/model")
+#   config.set_provider_option("QNN", "htp_performance_mode", "burst")
+#   model = og.Model(config)
+model = og.Model("/path/to/model")
+tokenizer = og.Tokenizer(model)
+tokenizer_stream = tokenizer.create_stream()
+
+params = og.GeneratorParams(model)
+params.set_search_options(max_length=200)
+generator = og.Generator(model, params)
+generator.append_tokens(tokenizer.encode("What color is the sky?"))
+
+while not generator.is_done():
+    generator.generate_next_token()
+    print(tokenizer_stream.decode(generator.get_next_tokens()[0]), end="", flush=True)
+print()
+```
+
+## Provider Options
+
+Options are set via `config.set_provider_option("QNN", key, value)`.
+
+| Option | Description | Values |
+|---|---|---|
+| `htp_performance_mode` | NPU power/performance profile | `burst`, `balanced`, `high_performance`, `power_saver` |
+| `vtcm_mb` | VTCM allocation size in MB | `"8"`, `"16"`, etc. |
+| `enable_htp_shared_memory_allocator` | Enable shared memory allocator for direct OGA/QNN tensor handoff | `"1"` to enable |