Add olive init cli

docs/source/blogs/index.md

@@ -4,6 +4,9 @@
 ```{gallery-grid}
 :grid-columns: 1 2 2 3
 
+- header: "{octicon}`terminal` Introducing olive init: An Interactive Wizard for Model Optimization"
+  content: "Get started with Olive in seconds — the new `olive init` wizard guides you step-by-step from model selection to a ready-to-run optimization command.<br/>{octicon}`arrow-right` [Introducing olive init](olive-init-cli.md)"
+
 - header: "{octicon}`cpu` Exploring Optimal Quantization Settings for Small Language Models"
   content: "An exploration of how Olive applies different quantization strategies such as GPTQ, mixed precision, and QuaRot to optimize small language models for efficiency and accuracy.<br/>{octicon}`arrow-right` [Exploring Optimal Quantization Settings for Small Language Models](quant-slms.md)"
 
@@ -16,6 +19,7 @@
 :maxdepth: 2
 :hidden:
 
+olive-init-cli.md
 quant-slms.md
 sd-lora.md
 ```

docs/source/blogs/olive-init-cli.md

@@ -0,0 +1,221 @@
+# Introducing `olive init`: An Interactive Wizard for Model Optimization
+
+*Author: Xiaoyu Zhang*
+*Created: 2026-04-06*
+
+Getting started with AI model optimization can be overwhelming — choosing the right exporter, quantization algorithm, precision, and target hardware involves navigating a complex decision space. The new **`olive init`** command solves this with an interactive, step-by-step wizard that guides you from model selection to a ready-to-run optimization command.
+
+---
+
+## Why `olive init`?
+
+Olive offers a powerful set of CLI commands — `optimize`, `quantize`, `capture-onnx-graph`, `finetune`, `diffusion-lora`, and more — each with many options. While this flexibility is great for experts, it can be daunting for newcomers. Common questions include:
+
+- *Which command should I use?*
+- *What exporter is best for my LLM?*
+- *Which quantization algorithm should I pick?*
+- *Do I need calibration data?*
+
+**`olive init`** answers all of these by walking you through a guided wizard. It asks the right questions, provides sensible defaults, and generates the exact CLI command or JSON config you need.
+
+---
+
+## Quick Start
+
+```bash
+pip install olive-ai
+olive init
+```
+
+That's it! The wizard launches in your terminal and walks you through every step.
+
+---
+
+## How It Works
+
+The wizard follows a simple 4-step flow:
+
+### Step 1: Choose Your Model Type
+
+```
+? What type of model do you want to optimize?
+❯ PyTorch (HuggingFace or local)
+  ONNX
+  Diffusers (Stable Diffusion, SDXL, Flux, etc.)
+```
+
+### Step 2: Specify Your Model
+
+Depending on the model type, you can provide:
+
+- **HuggingFace model name** (e.g., `meta-llama/Llama-3.1-8B`)
+- **Local directory path**
+- **AzureML registry path**
+- **PyTorch model with custom script**
+
+### Step 3: Configure Your Workflow
+
+This is where the wizard really shines. Based on your model type, it presents relevant operations and guides you through the configuration:
+
+::::{tab-set}
+
+:::{tab-item} PyTorch Models
+
+**Available operations:**
+
+| Operation | Description |
+|-----------|-------------|
+| Optimize | Export to ONNX + quantize + graph optimize (all-in-one) |
+| Export to ONNX | Convert to ONNX format using Model Builder, Dynamo, or TorchScript |
+| Quantize | Apply PyTorch quantization (RTN, GPTQ, AWQ, QuaRot, SpinQuant) |
+| Fine-tune | LoRA or QLoRA fine-tuning on custom datasets |
+
+For the **Optimize** operation, you can choose between:
+
+- **Auto Mode** — Olive automatically selects the best passes for your target hardware and precision
+- **Custom Mode** — Manually pick which operations to include (export, quantize, graph optimization) and configure each one
+
+:::
+
+:::{tab-item} ONNX Models
+
+**Available operations:**
+
+| Operation | Description |
+|-----------|-------------|
+| Optimize | Auto-select best passes for target hardware |
+| Quantize | Static, dynamic, block-wise RTN, HQQ, or BnB quantization |
+| Graph optimization | Apply ONNX graph-level optimizations |
+| Convert precision | FP32 → FP16 conversion |
+| Tune session params | Auto-tune ONNX Runtime inference parameters |
+
+:::
+
+:::{tab-item} Diffusers Models
+
+**Available operations:**
+
+| Operation | Description |
+|-----------|-------------|
+| Export to ONNX | Export diffusion pipeline for ONNX Runtime deployment |
+| LoRA Training | Fine-tune with LoRA on custom images (DreamBooth supported) |
+
+**Supported architectures:** SD 1.x/2.x, SDXL, SD3, Flux, Sana
+
+:::
+
+::::
+
+### Step 4: Choose Your Output
+
+```
+? What would you like to do?
+❯ Generate CLI command (copy and run later)
+  Generate configuration file (JSON, for olive run)
+  Run optimization now
+```
+
+You can generate the command to review first, save a reusable JSON config, or execute immediately.
+
+---
+
+## Examples
+
+### Example 1: Optimize a HuggingFace LLM for CPU with INT4
+
+```
+$ olive init
+
+Welcome to Olive Init! This wizard will help you optimize your model.
+
+? What type of model do you want to optimize? PyTorch (HuggingFace or local)
+? How would you like to specify your model? HuggingFace model name
+? Model name or path: Qwen/Qwen2.5-0.5B-Instruct
+? What do you want to do? Optimize model (export to ONNX + quantize + graph optimize)
+? How would you like to configure optimization? Auto Mode (recommended)
+? Select target device: CPU
+? Select target precision: INT4 (smallest size, best for LLMs)
+? Output directory: ./olive-output
+? What would you like to do? Generate CLI command (copy and run later)
+
+Generated command:
+
+  olive optimize -m Qwen/Qwen2.5-0.5B-Instruct --provider CPUExecutionProvider --precision int4 -o ./olive-output
+```
+
+### Example 2: Quantize a PyTorch Model with GPTQ
+
+```
+$ olive init
+
+? What type of model do you want to optimize? PyTorch (HuggingFace or local)
+? How would you like to specify your model? HuggingFace model name
+? Model name or path: meta-llama/Llama-3.1-8B
+? What do you want to do? Quantize only (PyTorch quantization)
+? Select quantization algorithm: GPTQ - High quality, requires calibration
+? Precision: int4
+? Calibration data source: Use default (wikitext-2)
+? Output directory: ./olive-output
+? What would you like to do? Run optimization now
+```
+
+### Example 3: Fine-tune with LoRA
+
+```
+$ olive init
+
+? What type of model do you want to optimize? PyTorch (HuggingFace or local)
+? How would you like to specify your model? HuggingFace model name
+? Model name or path: microsoft/Phi-4-mini-instruct
+? What do you want to do? Fine-tune model (LoRA, QLoRA)
+? Select fine-tuning method: LoRA (recommended)
+? LoRA rank (r): 64 (default)
+? LoRA alpha: 16
+? Training dataset: HuggingFace dataset
+? Dataset name: tatsu-lab/alpaca
+? Train split: train
+? How to construct training text? Use chat template
+? Max sequence length: 1024
+? Max training samples: 256
+? Torch dtype for training: bfloat16 (recommended)
+? Output directory: ./olive-output
+? What would you like to do? Generate CLI command (copy and run later)
+
+Generated command:
+
+  olive finetune -m microsoft/Phi-4-mini-instruct --method lora --lora_r 64 --lora_alpha 16 -d tatsu-lab/alpaca --train_split train --use_chat_template --max_seq_len 1024 --max_samples 256 --torch_dtype bfloat16 -o ./olive-output
+```
+
+### Example 4: Train a Diffusion LoRA with DreamBooth
+
+```
+$ olive init
+
+? What type of model do you want to optimize? Diffusers (Stable Diffusion, SDXL, Flux, etc.)
+? Select diffuser model variant: Stable Diffusion XL (SDXL)
+? Enter model name or path: stabilityai/stable-diffusion-xl-base-1.0
+? What do you want to do? LoRA Training (fine-tune on custom images)
+? LoRA rank (r): 16 (recommended)
+? LoRA alpha: 16
+? Training data source: Local image folder
+? Path to image folder: ./my-dog-photos
+? Enable DreamBooth training? Yes
+? Instance prompt: a photo of sks dog
+? Enable prior preservation? Yes
+? Class prompt: a photo of a dog
+? Max training steps: 1000 (recommended)
+? Output directory: ./olive-output
+? What would you like to do? Generate CLI command (copy and run later)
+
+Generated command:
+
+  olive diffusion-lora -m stabilityai/stable-diffusion-xl-base-1.0 --model_variant sdxl -r 16 --alpha 16 --lora_dropout 0.0 -d ./my-dog-photos --dreambooth --instance_prompt "a photo of sks dog" --with_prior_preservation --class_prompt "a photo of a dog" --num_class_images 200 --max_train_steps 1000 --learning_rate 1e-4 --train_batch_size 1 --gradient_accumulation_steps 4 --mixed_precision bf16 --lr_scheduler constant --lr_warmup_steps 0 -o ./olive-output
+```
+
+---
+
+## Related Resources
+
+- [Olive CLI Reference](https://microsoft.github.io/Olive/reference/cli.html)
+- [Olive Getting Started Guide](https://microsoft.github.io/Olive/getting-started/getting-started.html)
+- [Olive GitHub Repository](https://github.com/microsoft/Olive)

olive/cli/init/__init__.py

@@ -0,0 +1,30 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+from argparse import ArgumentParser
+
+from olive.cli.base import BaseOliveCLICommand
+
+
+class InitCommand(BaseOliveCLICommand):
+    @staticmethod
+    def register_subcommand(parser: ArgumentParser):
+        sub_parser = parser.add_parser(
+            "init",
+            help="Interactive wizard to configure and generate Olive optimization commands.",
+        )
+        sub_parser.add_argument(
+            "-o",
+            "--output_path",
+            type=str,
+            default="./olive-output",
+            help="Default output directory for the generated command. Default is ./olive-output.",
+        )
+        sub_parser.set_defaults(func=InitCommand)
+
+    def run(self):
+        from olive.cli.init.wizard import InitWizard
+
+        wizard = InitWizard(default_output_path=self.args.output_path)
+        wizard.start()