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docs: some updates for nano3 (#149)
* some fixes * generate colab notebooks
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‎docs/colab_notebooks/1-the-basics.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"id": "9f804f90",
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"id": "56daa304",
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"metadata": {},
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"source": [
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"# 🎨 Data Designer Tutorial: The Basics\n",
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"cell_type": "markdown",
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"id": "8734a74a",
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"metadata": {},
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"source": [
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"### ⚡ Colab Setup\n",
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{
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"cell_type": "code",
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"execution_count": null,
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{
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"cell_type": "code",
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"execution_count": null,
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{
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"cell_type": "markdown",
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"id": "0543d90e",
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"metadata": {},
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"### 📦 Import the essentials\n",
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{
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"cell_type": "code",
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"cell_type": "markdown",
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"### ⚙️ Initialize the Data Designer interface\n",
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{
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"cell_type": "code",
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"cell_type": "markdown",
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"id": "da9d9f06",
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"### 🎛️ Define model configurations\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"MODEL_ID = \"nvidia/nemotron-3-nano-30b-a3b\"\n",
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"\n",
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"# We choose this alias to be descriptive for our use case.\n",
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"MODEL_ALIAS = \"nemotron-nano-v2\"\n",
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"\n",
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"# This sets reasoning to False for the nemotron-nano-v2 model.\n",
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"SYSTEM_PROMPT = \"/no_think\"\n",
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"MODEL_ALIAS = \"nemotron-nano-v3\"\n",
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"\n",
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"model_configs = [\n",
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" ModelConfig(\n",
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" alias=MODEL_ALIAS,\n",
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" model=MODEL_ID,\n",
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" provider=MODEL_PROVIDER,\n",
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" inference_parameters=ChatCompletionInferenceParams(\n",
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" temperature=0.5,\n",
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" temperature=1.0,\n",
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" top_p=1.0,\n",
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" max_tokens=1024,\n",
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" max_tokens=2048,\n",
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" extra_body={\"chat_template_kwargs\": {\"enable_thinking\": False}},\n",
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" ),\n",
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" )\n",
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"]"
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]
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},
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"cell_type": "markdown",
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"id": "a968637c",
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"### 🏗️ Initialize the Data Designer Config Builder\n",
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"cell_type": "code",
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"## 🎲 Getting started with sampler columns\n",
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{
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"cell_type": "markdown",
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"Let's start designing our product review dataset by adding product category and subcategory columns.\n"
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"Next, let's add samplers to generate data related to the customer and their review.\n"
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"## 🦜 LLM-generated columns\n",
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" \"on products related to '{{ product_subcategory }}'. The target age range of the ideal customer is \"\n",
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" \"{{ target_age_range }} years old. Respond with only the product name, no other text.\"\n",
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" ),\n",
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")\n",
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" \"You are a customer named {{ customer.first_name }} from {{ customer.city }}, {{ customer.state }}. \"\n",
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" \"You are {{ customer.age }} years old and recently purchased a product called {{ product_name }}. \"\n",
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" \"Write a review of this product, which you gave a rating of {{ number_of_stars }} stars. \"\n",
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" \"The style of the review should be '{{ review_style }}'.\"\n",
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" \"The style of the review should be '{{ review_style }}'. \"\n",
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" \"Respond with only the review, no other text.\"\n",
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" ),\n",
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"### 🔁 Iteration is key – preview the dataset!\n",
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"### 📊 Analyze the generated data\n",
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"### 🆙 Scale up!\n",
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"## ⏭️ Next Steps\n",

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