Fixed all "Open in Colab" absolute paths (#850)

3p-integrations/groq/llama3_cookbook_groq.ipynb

@@ -7,7 +7,8 @@
    "source": [
     "# Llama 3 Cookbook with LlamaIndex and Groq\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/llama_api_providers/llama3_cookbook_groq.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/groq/llama3_cookbook_groq.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "\n",
     "\n",
     "Meta developed and released the Meta [Llama 3](https://ai.meta.com/blog/meta-llama-3/) family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks.\n",
     "\n",

3p-integrations/langchain/langgraph_rag_agent.ipynb

@@ -5,7 +5,7 @@
    "id": "6912ab05-f66a-40a9-a4a5-4deb80d2e0d9",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/langchain/langgraph_rag_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/langchain/langgraph_rag_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

3p-integrations/langchain/langgraph_rag_agent_local.ipynb

@@ -5,7 +5,7 @@
    "id": "1f53f753-12c6-4fac-b910-6e96677d8a49",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/langchain/langgraph_rag_agent_local.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/langchain/langgraph_rag_agent_local.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

3p-integrations/langchain/langgraph_tool_calling_agent.ipynb

@@ -5,7 +5,7 @@
    "id": "8ac4ba3b-c438-4f2e-8f52-39846beb5642",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/langchain/langgraph_tool_calling_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/langchain/langgraph_tool_calling_agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

3p-integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L2_Tool_Calling.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L2_Tool_Calling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L2_Tool_Calling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 2 Tool Calling](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/3/tool-calling) to using Llama 3. It shows how to use Llama 3 to not only pick a function to execute, but also infer an argument to pass through the function.\n",
     "\n",

3p-integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L3_Building_an_Agent_Reasoning_Loop.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 3 Building an Agent Reasoning Loop](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/4/building-an-agent-reasoning-loop) to using Llama 3. It shows how to define a complete agent reasoning loop to reason over tools and multiple steps on a complex question the user asks about a single document while maintaining memory.\n",
     "\n",

3p-integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L4_Building_a_Multi-Document_Agent.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L4_Building_a_Multi-Document_Agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/llamaindex/dlai_agentic_rag/Building_Agentic_RAG_with_Llamaindex_L4_Building_a_Multi-Document_Agent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "This notebook ports the DeepLearning.AI short course [Building Agentic RAG with Llamaindex Lesson 4 Building a Multi-Document Agent](https://learn.deeplearning.ai/courses/building-agentic-rag-with-llamaindex/lesson/5/building-a-multi-document-agent) to using Llama 3. It shows how to use an agent to handle multiple documents and increasing degrees of complexity.\n",
     "\n",

3p-integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Generating Knowledge Graphs with LLMs and Structured Outputs\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/togetherai/knowledge_graphs_with_structured_outputs.ipynb)"
    ]
   },
   {
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -38,52 +38,7 @@
     "id": "DFAjay1FZVrn",
     "outputId": "d4b17b31-c125-4de5-ad54-6d4d08d81eaa"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: together in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (1.3.3)\n",
-      "Requirement already satisfied: aiohttp<4.0.0,>=3.9.3 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (3.10.10)\n",
-      "Requirement already satisfied: click<9.0.0,>=8.1.7 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (8.1.7)\n",
-      "Requirement already satisfied: eval-type-backport<0.3.0,>=0.1.3 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (0.2.0)\n",
-      "Requirement already satisfied: filelock<4.0.0,>=3.13.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (3.16.1)\n",
-      "Requirement already satisfied: numpy>=1.23.5 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (1.26.4)\n",
-      "Requirement already satisfied: pillow<11.0.0,>=10.3.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (10.4.0)\n",
-      "Requirement already satisfied: pyarrow>=10.0.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (18.0.0)\n",
-      "Requirement already satisfied: pydantic<3.0.0,>=2.6.3 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (2.9.2)\n",
-      "Requirement already satisfied: requests<3.0.0,>=2.31.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (2.32.3)\n",
-      "Requirement already satisfied: rich<14.0.0,>=13.8.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (13.9.3)\n",
-      "Requirement already satisfied: tabulate<0.10.0,>=0.9.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (0.9.0)\n",
-      "Requirement already satisfied: tqdm<5.0.0,>=4.66.2 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (4.66.6)\n",
-      "Requirement already satisfied: typer<0.13,>=0.9 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from together) (0.12.5)\n",
-      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (2.4.3)\n",
-      "Requirement already satisfied: aiosignal>=1.1.2 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (1.3.1)\n",
-      "Requirement already satisfied: attrs>=17.3.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (24.2.0)\n",
-      "Requirement already satisfied: frozenlist>=1.1.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (1.5.0)\n",
-      "Requirement already satisfied: multidict<7.0,>=4.5 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (6.1.0)\n",
-      "Requirement already satisfied: yarl<2.0,>=1.12.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (1.17.1)\n",
-      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from aiohttp<4.0.0,>=3.9.3->together) (4.0.3)\n",
-      "Requirement already satisfied: annotated-types>=0.6.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.6.3->together) (0.7.0)\n",
-      "Requirement already satisfied: pydantic-core==2.23.4 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.6.3->together) (2.23.4)\n",
-      "Requirement already satisfied: typing-extensions>=4.6.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from pydantic<3.0.0,>=2.6.3->together) (4.12.2)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from requests<3.0.0,>=2.31.0->together) (3.4.0)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from requests<3.0.0,>=2.31.0->together) (3.10)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from requests<3.0.0,>=2.31.0->together) (2.2.3)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from requests<3.0.0,>=2.31.0->together) (2024.8.30)\n",
-      "Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from rich<14.0.0,>=13.8.1->together) (3.0.0)\n",
-      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from rich<14.0.0,>=13.8.1->together) (2.18.0)\n",
-      "Requirement already satisfied: shellingham>=1.3.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from typer<0.13,>=0.9->together) (1.5.4)\n",
-      "Requirement already satisfied: mdurl~=0.1 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich<14.0.0,>=13.8.1->together) (0.1.2)\n",
-      "Requirement already satisfied: propcache>=0.2.0 in /Users/jeffxtang/anaconda3/envs/llama-recipes/lib/python3.10/site-packages (from yarl<2.0,>=1.12.0->aiohttp<4.0.0,>=3.9.3->together) (0.2.0)\n",
-      "Collecting graphviz\n",
-      "  Using cached graphviz-0.20.3-py3-none-any.whl.metadata (12 kB)\n",
-      "Using cached graphviz-0.20.3-py3-none-any.whl (47 kB)\n",
-      "Installing collected packages: graphviz\n",
-      "Successfully installed graphviz-0.20.3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install together\n",
     "!pip install graphviz"

3p-integrations/togetherai/pdf_to_podcast_using_llama_on_together.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/3p_integrations/togetherai/pdf_to_podcast_using_llama_on_together.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/3p-integrations/togetherai/pdf_to_podcast_using_llama_on_together.ipynb)"
    ]
   },
   {

README.md

@@ -17,7 +17,7 @@ This repository covers the most popular community approaches, use-cases and the
 > * [Text to SQL](./end-to-end-use-cases/coding/text2sql/)
 
 
-> Note: We recently did a refactor of the repo, [archive-main](https://github.com/meta-llama/llama-recipes/tree/archive-main) is a snapshot branch from before the refactor
+> Note: We recently did a refactor of the repo, [archive-main](https://github.com/meta-llama/llama-cookbook/tree/archive-main) is a snapshot branch from before the refactor
 
 ## Repository Structure:
 
@@ -44,7 +44,7 @@ A: Checkout the Fine-Tuning FAQ [here](./src/docs/)
 
 - Q: Some links are broken/folders are missing: 
 
-A: We recently did a refactor of the repo, [archive-main](https://github.com/meta-llama/llama-recipes/tree/archive-main) is a snapshot branch from before the refactor
+A: We recently did a refactor of the repo, [archive-main](https://github.com/meta-llama/llama-cookbook/tree/archive-main) is a snapshot branch from before the refactor
 
 - Where can we find details about the latest models?
 

end-to-end-use-cases/agents/DeepLearningai_Course_Notebooks/AI_Agentic_Design_Patterns_with_AutoGen_L4_Tool_Use_and_Conversational_Chess.ipynb

@@ -5,7 +5,7 @@
    "id": "7a4b75bb-d60a-41e3-abca-1ca0f0bf1201",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks/AI_Agentic_Design_Patterns_with_AutoGen_L4_Tool_Use_and_Conversational_Chess.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/agents/DeepLearningai_Course_Notebooks/AI_Agentic_Design_Patterns_with_AutoGen_L4_Tool_Use_and_Conversational_Chess.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

end-to-end-use-cases/agents/DeepLearningai_Course_Notebooks/AI_Agents_in_LangGraph_L1_Build_an_Agent_from_Scratch.ipynb

@@ -5,7 +5,7 @@
    "id": "de56ee05-3b71-43c9-8cbf-6ad9b3233f38",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks/AI_Agents_in_LangGraph_L1_Build_an_Agent_from_Scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/agents/DeepLearningai_Course_Notebooks/AI_Agents_in_LangGraph_L1_Build_an_Agent_from_Scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

end-to-end-use-cases/agents/DeepLearningai_Course_Notebooks/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/agents/DeepLearningai_Course_Notebooks/Building_Agentic_RAG_with_Llamaindex_L1_Router_Engine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

end-to-end-use-cases/agents/DeepLearningai_Course_Notebooks/Functions_Tools_and_Agents_with_LangChain_L1_Function_Calling.ipynb

@@ -5,7 +5,7 @@
    "id": "2ba1b4ef-3b96-4e7e-b5d0-155b839db73c",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks/Functions_Tools_and_Agents_with_LangChain_L1_Function_Calling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/agents/DeepLearningai_Course_Notebooks/Functions_Tools_and_Agents_with_LangChain_L1_Function_Calling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

end-to-end-use-cases/coding/text2sql/quickstart.ipynb

@@ -5,7 +5,7 @@
    "id": "e8cba0b6",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/use_cases/coding/text2sql/quickstart.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>  \n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/coding/text2sql/quickstart.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>  \n",
     "\n",
     "## Quick Demo of Text2SQL Using Llama 3.3\n",
     "\n",

end-to-end-use-cases/live_data.ipynb

@@ -5,7 +5,7 @@
    "id": "30eb1704-8d76-4bc9-9308-93243aeb69cb",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/use_cases/LiveData.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/live_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "## This demo app shows:\n",
     "* How to use LlamaIndex, an open source library to help you build custom data augmented LLM applications\n",

end-to-end-use-cases/responsible_ai/llama_guard/llama_guard_customization_via_prompting_and_fine_tuning.ipynb

@@ -15,7 +15,7 @@
    "source": [
     "# Llama Guard 3 Customization: Taxonomy Customization, Zero/Few-shot prompting, Evaluation and Fine Tuning \n",
     "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/responsible_ai/llama_guard/llama_guard_customization_via_prompting_and_fine_tuning.ipynb\">\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/responsible_ai/llama_guard/llama_guard_customization_via_prompting_and_fine_tuning.ipynb\">\n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
     "</a>\n",
     "\n",

end-to-end-use-cases/responsible_ai/llama_guard/llama_guard_text_and_vision_inference.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Llama Guard 3 Text & Vision update\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/responsible_ai/llama_guard/llama_guard_text_and_vision_inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/responsible_ai/llama_guard/llama_guard_text_and_vision_inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "In this notebook we show simple inference scripts using the [transformers](https://github.com/huggingface/transformers) library, from HuggingFace. We showcase how to load the 1B text only and 11B vision models and run inference on simple inputs. For details on the models, refer to their corresponding model cards:\n",
     "* [Llama Guard 3 1B](https://github.com/meta-llama/PurpleLlama/blob/main/Llama-Guard3/1B/MODEL_CARD.md)\n",

end-to-end-use-cases/video_summary.ipynb

@@ -5,7 +5,7 @@
    "id": "30b1235c-2f3e-4628-9c90-30385f741550",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/use_cases/VideoSummary.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/video_summary.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "## This demo app shows:\n",
     "* How to use LangChain's YoutubeLoader to retrieve the caption in a YouTube video\n",

getting-started/Prompt_Engineering_with_Llama.ipynb

@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/Prompt_Engineering_with_Llama_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/getting-started/Prompt_Engineering_with_Llama.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "# Prompt Engineering with Llama\n",
     "\n",

getting-started/RAG/hello_llama_cloud.ipynb

@@ -5,7 +5,7 @@
    "id": "1c1ea03a-cc69-45b0-80d3-664e48ca6831",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/use_cases/RAG/HelloLlamaCloud.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/getting-started/RAG/hello_llama_cloud.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
     "\n",
     "## This demo app shows:\n",
     "* How to run Llama 3.1 in the cloud hosted on Replicate\n",
@@ -37,7 +37,7 @@
     "!pip install sentence-transformers\n",
     "!pip install faiss-cpu\n",
     "!pip install bs4\n",
-    "!pip install replicate",
+    "!pip install replicate\n",
     "!pip install langchain-community"
    ]
   },

getting-started/build_with_Llama_3_2.ipynb

@@ -5,7 +5,7 @@
    "id": "42939a0f",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/build_with_Llama_3_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/getting-started/build_with_Llama_3_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

getting-started/finetuning/quickstart_peft_finetuning.ipynb

@@ -8,7 +8,7 @@
     "Copyright (c) Meta Platforms, Inc. and affiliates.\n",
     "This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.\n",
     "\n",
-    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/finetuning/quickstart_peft_finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/meta-llama/llama-cookbook/blob/main/getting-started/finetuning/quickstart_peft_finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {
@@ -217,19 +217,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ubuntu/llama-recipes/src/llama_recipes/model_checkpointing/checkpoint_handler.py:17: DeprecationWarning: `torch.distributed._shard.checkpoint` will be deprecated, use `torch.distributed.checkpoint` instead\n",
-      "  from torch.distributed._shard.checkpoint import (\n",
-      "Preprocessing dataset: 100%|██████████| 14732/14732 [00:02<00:00, 5872.02it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from llama_recipes.configs.datasets import samsum_dataset\n",
     "from llama_recipes.utils.dataset_utils import get_dataloader\n",
@@ -283,45 +273,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ubuntu/llama-recipes/src/llama_recipes/utils/train_utils.py:92: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
-      "  scaler = torch.cuda.amp.GradScaler()\n",
-      "/home/ubuntu/miniconda3/envs/llama/lib/python3.11/site-packages/torch/cuda/memory.py:343: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats.\n",
-      "  warnings.warn(\n",
-      "Training Epoch: 1:   0%|\u001b[34m          \u001b[0m| 0/319 [00:00<?, ?it/s]huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
-      "To disable this warning, you can either:\n",
-      "\t- Avoid using `tokenizers` before the fork if possible\n",
-      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
-      "/home/ubuntu/llama-recipes/src/llama_recipes/utils/train_utils.py:151: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
-      "  with autocast():\n",
-      "/home/ubuntu/miniconda3/envs/llama/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
-      "  return fn(*args, **kwargs)\n",
-      "/home/ubuntu/miniconda3/envs/llama/lib/python3.11/site-packages/bitsandbytes/autograd/_functions.py:316: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
-      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
-      "/home/ubuntu/miniconda3/envs/llama/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
-      "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
-      "Training Epoch: 1/1, step 1278/1279 completed (loss: 0.28094857931137085): : 320it [2:08:50, 24.16s/it]                      4.21s/it]  \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Max CUDA memory allocated was 15 GB\n",
-      "Max CUDA memory reserved was 16 GB\n",
-      "Peak active CUDA memory was 15 GB\n",
-      "CUDA Malloc retries : 0\n",
-      "CPU Total Peak Memory consumed during the train (max): 2 GB\n",
-      "Epoch 1: train_perplexity=1.3404, train_epoch_loss=0.2930, epoch time 7730.981359725998s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch.optim as optim\n",
     "from llama_recipes.utils.train_utils import train\n",