Merge branch 'main' into ewt/extract-reasoning-content

Author: eric-tramel

docs/blog/.authors.yml

@@ -0,0 +1,5 @@
+authors:
+  nvidia:
+    name: NVIDIA NeMo Data Designer Team
+    description: NeMo Data Designer Core Team
+    avatar: https://avatars.githubusercontent.com/u/1728152?s=200&v=4

docs/blog/index.md

@@ -0,0 +1,5 @@
+# Dev Notes
+
+Welcome to NeMo Data Designer Dev Notes! Here you'll find in-depth guides, tutorials, and insights about synthetic data generation.
+
+<!-- Dev notes will automatically appear below -->

docs/blog/posts/welcome.md

@@ -0,0 +1,26 @@
+---
+date: 2026-01-22
+authors:
+  - nvidia
+---
+
+# Welcome to Data Designer Dev Notes
+
+We're excited to launch the Data Designer Dev Notes section!
+
+<!-- more -->
+
+This space will feature in-depth technical articles, best practices, and real-world case studies from the Data Designer team and community.
+
+## What to Expect
+
+- **Deep Dives**: Detailed explorations of Data Designer's capabilities and architecture
+- **Best Practices**: Tips and patterns for building high-quality synthetic datasets
+- **Use Cases**: Real-world applications and success stories from the community
+- **Research Highlights**: Insights from cutting-edge work in synthetic data generation
+
+## Stay Tuned
+
+Watch this space for technical deep dives into synthetic data generation, model customization, and more. We'll be sharing insights from our work and the broader community.
+
+In the meantime, check out our [Welcome guide](../../index.md) and [tutorial notebooks](../../notebooks/README.md) to get started with Data Designer.

docs/colab_notebooks/1-the-basics.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2af6c991",
+   "id": "c79eea7a",
    "metadata": {},
    "source": [
     "# 🎨 Data Designer Tutorial: The Basics\n",
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e365f58",
+   "id": "2476f160",
    "metadata": {},
    "source": [
     "### 📦 Import Data Designer\n",
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6b60c35",
+   "id": "3646f62e",
    "metadata": {},
    "source": [
     "### ⚡ Colab Setup\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "352ad006",
+   "id": "3348e5c8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d48fcd7d",
+   "id": "19cd9249",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7fa5c652",
+   "id": "5a6d13a9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,20 +76,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c70454d5",
+   "id": "d445af5b",
    "metadata": {},
    "source": [
     "### ⚙️ Initialize the Data Designer interface\n",
     "\n",
-    "- `DataDesigner` is the main object is responsible for managing the data generation process.\n",
+    "- `DataDesigner` is the main object responsible for managing the data generation process.\n",
     "\n",
     "- When initialized without arguments, the [default model providers](https://nvidia-nemo.github.io/DataDesigner/latest/concepts/models/default-model-settings/) are used.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bb2765d2",
+   "id": "4df0031d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8cd5279b",
+   "id": "0f69b576",
    "metadata": {},
    "source": [
     "### 🎛️ Define model configurations\n",
@@ -115,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cd90811a",
+   "id": "65d9be99",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca306aca",
+   "id": "72582d09",
    "metadata": {},
    "source": [
     "### 🏗️ Initialize the Data Designer Config Builder\n",
@@ -160,7 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8dda0de2",
+   "id": "8d7992b4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2bc4a65c",
+   "id": "741a15a0",
    "metadata": {},
    "source": [
     "## 🎲 Getting started with sampler columns\n",
@@ -186,7 +186,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5bc65ad1",
+   "id": "c3879c70",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "34f81d0b",
+   "id": "1575ef81",
    "metadata": {},
    "source": [
     "Let's start designing our product review dataset by adding product category and subcategory columns.\n"
@@ -204,7 +204,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2318c75a",
+   "id": "87a88d7b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -285,7 +285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8c68da2",
+   "id": "8c74b738",
    "metadata": {},
    "source": [
     "Next, let's add samplers to generate data related to the customer and their review.\n"
@@ -294,7 +294,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b8cb78e5",
+   "id": "4eb1da1f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57b604d5",
+   "id": "4324d869",
    "metadata": {},
    "source": [
     "## 🦜 LLM-generated columns\n",
@@ -346,7 +346,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b615760d",
+   "id": "1302a503",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eeb124f7",
+   "id": "7cf8241b",
    "metadata": {},
    "source": [
     "### 🔁 Iteration is key – preview the dataset!\n",
@@ -399,7 +399,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fffc9d8e",
+   "id": "6fc6cf39",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,7 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "643ee6a1",
+   "id": "c929e068",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -420,7 +420,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "69dfb926",
+   "id": "dfb04e2a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -430,7 +430,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f38ca0d",
+   "id": "adb879da",
    "metadata": {},
    "source": [
     "### 📊 Analyze the generated data\n",
@@ -443,7 +443,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a044678d",
+   "id": "ff58dd9f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,7 +453,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c7147ab",
+   "id": "57c7355d",
    "metadata": {},
    "source": [
     "### 🆙 Scale up!\n",
@@ -466,7 +466,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d6a92b76",
+   "id": "df49db99",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,7 +476,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fb888621",
+   "id": "2bbc48dd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -489,7 +489,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "521d34ae",
+   "id": "dc0673fa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -501,7 +501,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "910b21a2",
+   "id": "7688217b",
    "metadata": {},
    "source": [
     "## ⏭️ Next Steps\n",