Merge pull request #155 from MoritzLaurer/add-names-to-enterprise-coo…

…kbook

add authors to enterprise cookbooks

notebooks/en/enterprise_cookbook_argilla.ipynb

@@ -6,6 +6,14 @@
    "metadata": {},
    "source": [
     "# Data Annotation with Argilla Spaces\n",
+    "_Authored by: [Moritz Laurer](https://huggingface.co/MoritzLaurer)_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bb4f796",
+   "metadata": {},
+   "source": [
     "This notebook illustrates the workflow for systematically evaluating LLM outputs and creating LLM training data. You can start by using this notebook for evaluating the zeroshot performance of your favourite LLM on your task without any fine-tuning. If you want to improve performance, you can then easily reuse this workflow to create training data.\n",
     "\n",
     "**Example use-case: code generation.** For this tutorial we demonstrate how to create high quality test & train data for *code generation tasks*. The same workflow can, however, be adapted to any other task that's relevant for your specific use-case. \n",

notebooks/en/enterprise_cookbook_dev_spaces.md

@@ -1,5 +1,7 @@
 
 # Interactive Development In HF Spaces
+_Authored by: [Moritz Laurer](https://huggingface.co/MoritzLaurer)_
+
 
 Services like Google Colab or Kaggle Notebooks have made it dramatically easier for people to access compute in easy-to-use Jupyter notebooks in the browser. Unfortunately, these services also have several limitations: 
 - GPUs are unstable, and a training job can be canceled right before it finishes.

notebooks/en/enterprise_dedicated_endpoints.ipynb

@@ -6,15 +6,22 @@
    "metadata": {},
    "source": [
     "# Inference Endpoints (dedicated) \n",
-    "\n",
+    "_Authored by: [Moritz Laurer](https://huggingface.co/MoritzLaurer)_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d1e22fa",
+   "metadata": {},
+   "source": [
     "Have you ever wanted to create your own machine learning API? That's what we will do in this recipe with the [HF Dedicated Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index). Inference Endpoints enable you to pick any of the hundreds of thousands of models on the HF Hub, create your own API on a deployment platform you control, and on hardware you choose.\n",
     "\n",
     "[Serverless Inference APIs](link-to-recipe) are great for initial testing, but they are limited to a pre-configured selection of popular models and they are rate limited, because the serverless API's hardware is used by many users at the same time. With a Dedicated Inference Endpoint, you can customize the deployment of your model and the hardware is exclusively dedicated to you. \n",
     "\n",
     "In this recipe, we will: \n",
     "- Create an Inference Endpoint via a simple UI and send standard HTTP requests to the Endpoint\n",
     "- Create and manage different Inference Endpoints programmatically with the `huggingface_hub` library\n",
-    "- Cover three use-cases: text generation with an LLM, image generation with Stable Diffusion, and reasoning over images with Idefics2. \n"
+    "- Cover three use-cases: text generation with an LLM, image generation with Stable Diffusion, and reasoning over images with Idefics2. "
    ]
   },
   {

notebooks/en/enterprise_hub_serverless_inference_api.ipynb

@@ -5,7 +5,13 @@
    "metadata": {},
    "source": [
     "# Serverless Inference API\n",
-    "\n",
+    "_Authored by: [Andrew Reed](https://huggingface.co/andrewrreed)_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Hugging Face provides a [Serverless Inference API](https://huggingface.co/docs/api-inference/index) as a way for users to quickly test and evaluate thousands of publicly accessible (or your own privately permissioned) machine learning models with simple API calls ***for free***!\n",
     "\n",
     "In this notebook recipe, we'll demonstrate several different ways you can query the Serverless Inference API while exploring various tasks including: \n",