[ggma] Add gyu (ggma yielding utility) tool

Implement gyu CLI tool to automate GGMA model package creation:

- Merge prefill.py and decode.py into unified export.py
- Create modular gpm tool structure:
  - gyu/init.py: Setup venv, install deps (CPU-only torch), clone TICO, extract o2o tools
  - gyu/import.py: Download complete model from HuggingFace
  - gyu/export.py: Run conversion pipeline and create .ggma package
  - gyu/common.py: Shared utilities and constants
  - gyu/clean.py: Remove building directory
  - gyu/gyu: Bash wrapper to dispatch commands

Documentation:
- Rename README.md → DEVELOPER.md (technical guide)
- Add USER.md (user-facing guide)

Author: Sanggyu Lee

runtime/ggma/examples/generate_text/DEVELOPER.md

@@ -0,0 +1,130 @@
+# TinyLlama Text Generation Developer Guide
+
+This document provides a detailed technical guide for generating, processing, and optimizing the TinyLlama text-generation model. For basic usage, see [USER.md](USER.md).
+
+## Summary
+
+1. Set up the environment and install dependencies.
+2. Generate the initial `prefill` and `decode` Circle model files.
+3. Run the pipeline to optimize, reshape, and prune the model, producing a final `decode.circle` ready for inference.
+
+## Prerequisites
+
+### 1. Python virtual environment
+```bash
+$ cd runtime/ggma/examples/generate_text/
+$ python3 -m venv _
+$ source _/bin/activate
+```
+
+### 2. Prepare [gyu](tools/gyu/README.md) and o2o tools
+Install dependencies and setup `o2o` tools (similar to what `tools/gyu/init.py` does).
+
+> **Note**: We install the CPU version of `torch` first because `gyu` depends on `TICO`, which by default pulls in the large NVIDIA version of `torch`. Installing the CPU version beforehand prevents this.
+
+```bash
+# 1. Install torch (CPU) and gyu requirements
+$ pip install torch --index-url https://download.pytorch.org/whl/cpu
+$ pip install -r tools/gyu/requirements.txt
+
+# 2. Fetch o2o tools from PR #16233
+$ git fetch origin pull/16233/head:pr-16233
+$ git checkout pr-16233 -- tools/o2o
+$ chmod +x tools/o2o/*.py
+
+# 3. Add tools to PATH
+$ export PATH=$PWD/tools/o2o:$PWD/tools/gyu:$PATH
+```
+
+
+
+## Generating Model Files
+
+### 1. Install model dependencies
+```bash
+$ pip install -r tinyllama/tinyllama.requirements
+```
+
+### 2. Create the prefill and decode Circle model files
+```bash
+$ python tinyllama/tinyllama.py --mode prefill   # Generates prefill.circle
+$ python tinyllama/tinyllama.py --mode decode    # Generates decode_.circle
+```
+
+Verify the generated files:
+```bash
+$ ls -lh *.circle
+-rw-rw-r-- 1 gyu gyu 18M Nov 14 14:09 decode_.circle
+-rw-rw-r-- 1 gyu gyu 18M Nov 14 14:09 prefill.circle
+```
+
+### 3. Update `tinyllama.decode.circle`
+Fuse attention and normalize KV-cache inputs for the decode model.
+
+```bash
+$ fuse.attention.py < decode_.circle \
+    | reshape.io.py input --by_shape [1,16,30,4] [1,16,32,4] \
+    | transpose.io.kvcache.py > decode.circle
+```
+
+### 4. Merge prefill and decode circles
+Merge the models, retype input IDs, and clean up.
+
+```bash
+$ merge.circles.py prefill.circle decode.circle \
+    | fuse.bmm_lhs_const.py \
+    | downcast.input_ids.py \
+    | gc.py > model.circle
+```
+
+Verify final model files:
+```bash
+$ ls -l {decode,prefill,model}.circle
+-rw-rw-r-- 1 gyu gyu 18594868 Nov 22 17:26 decode.circle
+-rw-rw-r-- 1 gyu gyu 18642052 Nov 22 07:53 prefill.circle
+-rw-rw-r-- 1 gyu gyu 18629520 Nov 22 17:28 model.circle
+```
+
+## Create a GGMA package
+
+1. Create the package root directory and move `model.circle` there:
+```bash
+$ cd runtime/ggma/examples/generate_text
+$ mkdir tinyllama
+$ mv model.circle tinyllama/
+```
+
+2. Copy the tokenizer files (replace `{your_snapshot}` with the actual snapshot hash):
+```bash
+$ cp -L ~/.cache/huggingface/hub/models--Maykeye--TinyLLama-v0/snapshots/{your_snapshot}/tokenizer.* tinyllama/
+$ cp -L ~/.cache/huggingface/hub/models--Maykeye--TinyLLama-v0/snapshots/{your_snapshot}/config.json tinyllama/
+```
+
+```bash
+$ tree tinyllama/
+tinyllama/
+├── model.circle
+├── tokenizer.json
+└── tokenizer.model
+```
+
+## Build and run `ggma_run`
+
+```bash
+$ make -j$(nproc)
+$ make install
+```
+
+Check version:
+```bash
+$ Product/out/bin/ggma_run --version
+ggma_run v0.1.0 (nnfw runtime: v1.31.0)
+```
+
+Run the model:
+```bash
+$ Product/out/bin/ggma_run tinyllama
+prompt: Lily picked up a flower.
+generated: { 1100, 7899, 289, 826, 351, 600, 2439, 288, 266, 3653, 31843, 1100, 7899, 289, 1261, 291, 5869, 291, 1261, 31843, 1100, 7899 }
+detokenized: She liked to play with her friends in the park. She liked to run and jump and run. She liked
+```

runtime/ggma/examples/generate_text/README.md

@@ -0,0 +1,108 @@
+# Text Generation User Guide
+
+This guide shows how to create a GGMA package for text generation models using the `gyu` (GGMA Yielding Utility) tool.
+
+We use TinyLlama as an example throughout this guide.
+
+## Creating a GGMA package
+
+NOTE: Start from the ONE repository root directory.
+
+### 1. Initialize environment (one-time setup)
+
+Add [gyu](../../../../tools/gyu/README.md) to PATH:
+```bash
+$ export PATH=$PWD/tools/gyu:$PATH
+```
+
+Then, change directory to tinyllama example directory and run gyu init:
+```bash
+$ cd runtime/ggma/examples/generate_text/tinyllama
+$ gyu init
+```
+
+Python environment and o2o tools are prepared:
+```bash
+$ ls -ld o2o venv
+drwxrwxr-x 2 gyu gyu 4096 Nov 24 09:44 o2o
+drwxrwxr-x 6 gyu gyu 4096 Nov 24 09:42 venv
+```
+
+> **Note**: The `o2o` directory will be removed once [#13689](https://github.com/Samsung/ONE/pull/13689) is merged.
+
+### 2. Import model from HuggingFace
+
+```bash
+$ gyu import Maykeye/TinyLLama-v0
+```
+
+The HuggingFace model is downloaded to `build/tinyllama-v0/`:
+```
+$ tree build
+build
+└── tinyllama-v0
+    ├── backup
+    ├── config.json
+    ├── demo.py
+    ├── generation_config.json
+    ├── model.onnx
+    ├── model.safetensors
+    ├── pytorch_model.bin
+    ├── README.md
+    ├── special_tokens_map.json
+    ├── tokenizer_config.json
+    ├── tokenizer.json
+    ├── tokenizer.model
+    ├── train.ipynb
+    └── valid.py
+```
+
+### 3. Export to GGMA package
+
+```bash
+$ gyu export -s tinyllama.py
+```
+
+The GGMA package is generated in `build/out/`:
+```
+$ tree build/out
+build/out/
+├── config.json
+├── model.circle
+├── tokenizer.json
+└── tokenizer.model
+```
+
+## Building GGMA and Running a GGMA package
+
+NOTE: Start from the ONE repository root directory.
+
+### Build
+
+```bash
+$ make -j$(nproc)
+$ make install
+```
+
+For detailed build instructions, see the [ONE Runtime Build Guide](https://github.com/Samsung/ONE/blob/master/docs/runtime/README.md).
+
+Confirm that `ggma_run` is built and show its version:
+```bash
+$ Product/out/bin/ggma_run --version
+ggma_run v0.1.0 (nnfw runtime: v1.31.0)
+```
+
+### Run
+
+Execute the GGMA package (default prompt) to see a sample output:
+```bash
+$ Product/out/bin/ggma_run build/out
+prompt: Lily picked up a flower.
+generated: { 1100, 7899, 289, 826, 351, 600, 2439, 288, 266, 3653, 31843, 1100, 7899, 289, 1261, 291, 5869, 291, 1261, 31843, 1100, 7899 }
+detokenized: She liked to play with her friends in the park. She liked to run and jump and run. She liked
+```
+
+For detailed run instructions, see the [ggma_run guide](https://github.com/Samsung/ONE/blob/master/runtime/tests/tools/ggma_run/README.md).
+
+
+For developers who want to understand what happens under the hood, see [DEVELOPER.md](DEVELOPER.md).