From 40c0b455b5b167a3cd6dfcf1beaa593c039d5e5c Mon Sep 17 00:00:00 2001 From: David Berenstein Date: Wed, 24 Jan 2024 13:38:17 +0100 Subject: [PATCH] docs: 287 docs shorten titles tutorials and 277 update core example (#289) * docs: shorten doc titles docs: add core example * docs: update example on index page * docs: resolved bug in Google Colab * docs: removed plugs in tutorials * docs: updated some formatting --- docs/index.md | 4 +- ...clean-preference-dataset-judgelm-gpt.ipynb | 135 +++++++----------- .../create-a-math-preference-dataset.ipynb | 1 + ...notus-instructions-preferences-legal.ipynb | 104 +++++++------- mkdocs.yml | 5 +- 5 files changed, 107 insertions(+), 142 deletions(-) create mode 100644 docs/tutorials/create-a-math-preference-dataset.ipynb diff --git a/docs/index.md b/docs/index.md index 3100bbd404..41f4c36b2b 100644 --- a/docs/index.md +++ b/docs/index.md @@ -40,9 +40,9 @@ will create a `labeller` LLM using `OpenAILLM` with the `UltraFeedback` task for !!! note To run the script successfully, ensure you have assigned your OpenAI API key to the `OPENAI_API_KEY` environment variable. -For a more complete example, check out our awesome notebook on Google Colab: +For a more complete example, check out our awesome [tutorials](./tutorials/pipeline-notus-instructions-preferences-legal/) or the example below: -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1rO1-OlLFPBC0KPuXQOeMpZOeajiwNoMy?usp=sharing) +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/argilla-io/distilabel/blob/main/docs/tutorials/pipeline-notus-instructions-preferences-legal.ipynb) [![Open Source in Github](https://img.shields.io/badge/github-view%20source-black.svg)](https://github.com/argilla-io/distilabel/blob/main/docs/tutorials/pipeline-notus-instructions-preferences-legal.ipynb) ## Navigation diff --git a/docs/tutorials/clean-preference-dataset-judgelm-gpt.ipynb b/docs/tutorials/clean-preference-dataset-judgelm-gpt.ipynb index 7a7d60bca8..876db5de56 100644 --- a/docs/tutorials/clean-preference-dataset-judgelm-gpt.ipynb +++ b/docs/tutorials/clean-preference-dataset-judgelm-gpt.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# โœจ Clean a Preference Dataset with `JudgeLMTask` and `GPT4-turbo`\n", + "# ๐Ÿงผ Clean an existing preference dataset\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/argilla-io/distilabel/blob/main/docs/tutorials/clean-preference-dataset-judgelm-gpt.ipynb) [![Open Source in Github](https://img.shields.io/badge/github-view%20source-black.svg)](https://github.com/argilla-io/distilabel/blob/main/docs/tutorials/clean-preference-dataset-judgelm-gpt.ipynb)" ] @@ -50,35 +50,6 @@ "## Getting Started" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Running Argilla\n", - "\n", - "For this tutorial, you can use Argilla to visualize and annotate the dataset cleaned by distilabel. There are two main options for deploying and running Argilla:\n", - "\n", - "**Deploy Argilla on Hugging Face Spaces:** If you want to run tutorials with external notebooks (e.g., Google Colab) and you have an account on Hugging Face, you can deploy Argilla on Spaces with a few clicks:\n", - "\n", - "[![deploy on spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-to-spaces-lg.svg)](https://huggingface.co/new-space?template=argilla/argilla-template-space)\n", - "\n", - "For details about configuring your deployment, check the [official Hugging Face Hub guide](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla).\n", - "\n", - "**Launch Argilla using Argilla's quickstart Docker image**: This is the recommended option if you want [Argilla running on your local machine](../../getting_started/quickstart.ipynb). Note that this option will only let you run the tutorial locally and not with an external notebook service.\n", - "\n", - "For more information on deployment options, please check the Deployment section of the documentation.\n", - "\n", - "
\n", - "\n", - "Tip\n", - "\n", - "This tutorial is a Jupyter Notebook. There are two options to run it:\n", - "\n", - "- Use the Open in Colab button at the top of this page. This option allows you to run the notebook directly on Google Colab. Don't forget to change the runtime type to GPU for faster model training and inference.\n", - "- Download the .ipynb file by clicking on the View source link at the top of the page. This option allows you to download the notebook and run it on your local machine or on a Jupyter notebook tool of your choice.\n", - "
\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -99,7 +70,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install \"distilabel[openai,argilla]\" --upgrade" + "%pip install \"distilabel[openai]\" --upgrade" ] }, { @@ -125,7 +96,6 @@ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.metrics.pairwise import cosine_similarity\n", "\n", - "import argilla as rg\n", "from datasets import load_dataset\n", "from distilabel.llm import OpenAILLM\n", "from distilabel.pipeline import Pipeline\n", @@ -137,56 +107,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If you are running Argilla using the Docker quickstart image or Hugging Face Spaces, you need to init the Argilla client with the `URL` and `API_KEY`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Replace api_url with the url to your HF Spaces URL if using Spaces\n", - "# Replace api_key if you configured a custom API key\n", - "rg.init(\n", - " api_url=\"http://localhost:6900\",\n", - " api_key=\"owner.apikey\",\n", - " workspace=\"admin\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If youโ€™re running a private Hugging Face Space, you will also need to set the [HF_TOKEN](https://huggingface.co/settings/tokens) as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# # Set the HF_TOKEN environment variable\n", - "# import os\n", - "# os.environ['HF_TOKEN'] = \"your-hf-token\"\n", + "### Environment variables\n", "\n", - "# # Replace api_url with the url to your HF Spaces URL\n", - "# # Replace api_key if you configured a custom API key\n", - "# # Replace workspace with the name of your workspace\n", - "# rg.init(\n", - "# api_url=\"https://[your-owner-name]-[your_space_name].hf.space\",\n", - "# api_key=\"owner.apikey\",\n", - "# workspace=\"admin\",\n", - "# extra_headers={\"Authorization\": f\"Bearer {os.environ['HF_TOKEN']}\"},\n", - "# )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ "Finally, we will also need to provide a HF_TOKEN and the OPENAI_API_KEY to run the distilabel pipeline." ] }, @@ -505,7 +427,56 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Optionally, if you want to further filter and curate the dataset, you can push the dataset to [Argilla](https://github.com/argilla-io/argilla) as follows:" + "## Human Feedback with Argilla\n", + "\n", + "You can use the AI Feedback created by distilabel directly but we hae ve seen that enhancing it with human feedback will improve the quality of your LLM. We provide a `to_argilla` method which creates a dataset for Argilla along with out-of-the-box tailored metadata filters and semantic search to allow you to provide human feedback as quickly and engaging as possible. You can check [the Argilla docs](https://docs.argilla.io/en/latest/getting_started/quickstart_installation.html) to get it up and running." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, install it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install \"distilabel[argilla]\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are running Argilla using the Docker quickstart image or Hugging Face Spaces, you need to init the Argilla client with the URL and API_KEY:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import argilla as rg\n", + "\n", + "# Replace api_url with the url to your HF Spaces URL if using Spaces\n", + "# Replace api_key if you configured a custom API key\n", + "rg.init(\n", + " api_url=\"http://localhost:6900\",\n", + " api_key=\"owner.apikey\",\n", + " workspace=\"admin\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now push the dataset to Argilla as follows:" ] }, { diff --git a/docs/tutorials/create-a-math-preference-dataset.ipynb b/docs/tutorials/create-a-math-preference-dataset.ipynb new file mode 100644 index 0000000000..2db516ef5f --- /dev/null +++ b/docs/tutorials/create-a-math-preference-dataset.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"_7ptlRStN9Mw"},"source":["# ๐Ÿงฎ Create a mathematical preference dataset \n","\n","`distilabel` is a new AI Feedback (AIF) framework created by [Argilla](https://argilla.io/) that leverages the power of LLMs for generating synthetic datasets for preference or self-instruct. You can find more information in the links below:\n","\n","- GitHub: [argilla-io/distilabel](https://github.com/argilla-io/distilabel)\n","- Docs: [distilabel.argilla.io](http://distilabel.argilla.io)\n","\n","And also don't forget to follow us on social media to keep up to date with the latest news about Argilla and distilabel:\n","\n","- Twitter: [@argilla_io](https://twitter.com/argilla_io)\n","- LinkedIn: [Argilla](https://www.linkedin.com/company/argilla-io)\n","\n","## Demo\n","\n","In this demo, we will create a preference dataset that can be later used to fine-tune an LLM using DPO. First, we will define a list of math topics and we will create a pipeline for generating a list of instructions using `self-instruct` and OpenAI `gpt-3.5-turbo`. After that, we will create another pipeline in which we will ask `gpt-3.5-turbo` to generate 3 texts for each instruction, and finally we will ask it again to rate these responses, given us our preference dataset.\n","\n","### Setup\n","\n","For this tutorial you will need an API key associated to your OpenAI account. After that, you will need to create a Google Colab secret clicking the icon key in the left side bar and create a secret called `OPENAI_API_KEY` with your OpenAI API key as value.\n","\n","> Google Colab secrets has been released a few weeks ago and it's very useful to reuse and not leak your API Keys!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"46hsMToDUN5H"},"outputs":[],"source":["import os\n","from google.colab import userdata\n","\n","os.environ[\"OPENAI_API_KEY\"] = userdata.get(\"OPENAI_API_KEY\")"]},{"cell_type":"markdown","metadata":{"id":"XAUHMiOJPs93"},"source":["### Installing `distilabel`\n","\n","We will `distilabel` with the `openai` and `argilla` extras, to also install the `openai` and `argilla` clients that we will need later. In addition, we will install an extension for timing some cells."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14027,"status":"ok","timestamp":1701177676723,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"IpO8CKrn0hFL","outputId":"ab57f5fd-72de-495a-f7b8-98ac39915f9c"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m60.1/60.1 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m220.8/220.8 kB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m75.7/75.7 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m27.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m76.0/76.0 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","llmx 0.0.15a0 requires cohere, which is not installed.\n","llmx 0.0.15a0 requires tiktoken, which is not installed.\u001b[0m\u001b[31m\n","\u001b[0mtime: 330 ยตs (started: 2023-11-28 13:21:16 +00:00)\n"]}],"source":["%pip install distilabel[openai] ipython-autotime -qqq\n","%load_ext autotime"]},{"cell_type":"markdown","metadata":{"id":"xEgKfbaY0KTl"},"source":["### Instruction generation\n","\n","As mentioned above, we will first create a `Pipeline` for generating instructions using `self-instruct` and `gpt-3.5-turbo`. For that we will create an instance of `SelfInstructTask`, which defines a prompt template for generating instructions given an application description. We will also create an instance of `OpenAILLM` for using `gpt-3.5-turbo` and we will pass it the `SelfInstructTask` instance that we created before.\n","\n","> As we're passing a `Task` for generating texts to the `OpenAILLM` we can denominate this one as a `generator`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13753,"status":"ok","timestamp":1701177696395,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"ARP2gtEtS1yE","outputId":"0802295a-1d3f-4065-c157-5604a9f058fd"},"outputs":[{"name":"stdout","output_type":"stream","text":["time: 13.3 s (started: 2023-11-28 13:21:22 +00:00)\n"]}],"source":["from distilabel.tasks import SelfInstructTask\n","from distilabel.llm import OpenAILLM\n","from distilabel.pipeline import Pipeline"]},{"cell_type":"markdown","metadata":{"id":"2ytKVekTXHNA"},"source":["First of all, we will create a Hugging Face ๐Ÿค— `dataset` that will contain a single column called `input`. This column will contain the math topics from which we want our LLM to generate instructions.\n","\n","> It's important that the column is called `input`, because the `Task` that we will create later expects an input argument called `input`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":234,"status":"ok","timestamp":1701177706735,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"UIy6ihVox0aQ","outputId":"3d5b4e2e-d098-4e50-d0db-8eef501f53c3"},"outputs":[{"name":"stdout","output_type":"stream","text":["time: 19.3 ms (started: 2023-11-28 13:21:46 +00:00)\n"]}],"source":["from datasets import Dataset\n","\n","\n","math_topics = [\n"," \"Algebraic Expressions\",\n"," \"Linear Equations\",\n"," \"Quadratic Equations\",\n"," \"Polynomial Functions\",\n"," \"Rational Expressions\",\n"," \"Exponential Functions\",\n"," \"Logarithmic Functions\",\n"," \"Sequences and Series\",\n"," \"Matrices\",\n"," \"Determinants\",\n"," \"Complex Numbers\",\n"," \"Trigonometry\",\n"," \"Geometry\",\n"," \"Coordinate Geometry\",\n"," \"Vector Algebra\",\n"," \"Statistics\",\n"," \"Probability\",\n"," \"Calculus\",\n"," \"Differential Calculus\",\n"," \"Integral Calculus\",\n"," \"Limits and Continuity\",\n"," \"Differentiation\",\n"," \"Integration\",\n"," \"Theorems of Calculus\",\n"," \"Mathematical Reasoning\",\n"," \"Set Theory\",\n"," \"Number Theory\",\n"," \"Permutations and Combinations\",\n"," \"Binomial Theorem\",\n"," \"Arithmetic Progressions\",\n"," \"Geometric Progressions\",\n"," \"Harmonic Progressions\",\n"," \"Trigonometric Ratios\",\n"," \"Trigonometric Identities\",\n"," \"Inverse Trigonometric Functions\",\n"," \"Hyperbolic Functions\",\n"," \"Conic Sections\",\n"," \"Circle Geometry\",\n"," \"Ellipse Geometry\",\n"," \"Parabola Geometry\",\n"," \"Hyperbola Geometry\",\n"," \"Function Theory\",\n"," \"Graph Theory\",\n"," \"Differential Equations\",\n"," \"Mathematical Induction\",\n"," \"Discrete Mathematics\",\n","]\n","\n","dataset = Dataset.from_dict({\n"," \"input\": math_topics\n","})"]},{"cell_type":"markdown","metadata":{"id":"ZtM2ZZMdXbXg"},"source":["Next, we will a `SelfInstructTask` that will guide the `LLM` using the prompt to generate instructions from the given list of inputs.\n","\n","> All the `Task`s have two properties `input_args_names` and `output_args_names` that indicates which arguments expects as inputs and outputs will generate respectively."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":261,"status":"ok","timestamp":1701177711064,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"JUVqzpjEVxsX","outputId":"d8bbf771-7e8a-4ab9-8b43-05174d45aba9"},"outputs":[{"name":"stdout","output_type":"stream","text":["`SelfInstructTask`\n"," - Input arguments: ['input']\n"," - Output arguments: ['generations']\n","time: 692 ยตs (started: 2023-11-28 13:21:50 +00:00)\n"]}],"source":["application_description = (\n"," \"An AI assistant adept at answering a wide array of math, logic, and reasoning puzzles, trivia, \"\n"," \"and general questions. Users of this assistant love to ask the assistant to think and outlines \"\n"," \"the solutions step by step. It expects complete questions from users providing all the details \"\n"," \"to solve the proposed problem or respond to general knowledge questions. It covers general \"\n"," \"knowledge about math, puzzles, reasoning exercises, and real-life scenarios where math and \"\n"," \"reasoning are important.\"\n",")\n","\n","# by default `SelfInstructTask` will generate 5 instructions, but we can tweak\n","# this behaviour passing the `num_instructions` argument.\n","instruction_task = SelfInstructTask(\n"," application_description=application_description\n",")\n","\n","print(f\"`SelfInstructTask`\\n - Input arguments: {instruction_task.input_args_names}\\n - Output arguments: {instruction_task.output_args_names}\")"]},{"cell_type":"markdown","metadata":{"id":"YASgix42ZOvO"},"source":["Next step, we will create an `LLM`, in this case an instance of `OpenAILLM` as we want to use `gpt-3.5-turbo` to generate the instructions. We will pass the `instruction_task` for generating the prompts that we need for generating the instructions given the `input`s of our dataset."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":411,"status":"ok","timestamp":1701177715314,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"2Da8DkWJV1Lr","outputId":"8289af8d-6027-45e2-86ea-187a3479587c"},"outputs":[{"name":"stdout","output_type":"stream","text":["time: 497 ms (started: 2023-11-28 13:21:54 +00:00)\n"]}],"source":["instruction_generator = OpenAILLM(\n"," task=instruction_task,\n"," num_threads=8,\n"," max_new_tokens=1024,\n"," temperature=0.7\n",")"]},{"cell_type":"markdown","metadata":{"id":"u-2fzkY0ZoWc"},"source":["Finally, we will create a `Pipeline` to orchestrate the whole generation process. In this case we will only pass a `generator`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1701177717001,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"oNzYvsUMV768","outputId":"2aa55687-c3f4-4020-aed8-6574231c1fe0"},"outputs":[{"name":"stdout","output_type":"stream","text":["time: 576 ยตs (started: 2023-11-28 13:21:56 +00:00)\n"]}],"source":["pipeline = Pipeline(generator=instruction_generator)"]},{"cell_type":"markdown","metadata":{"id":"DcLzuTj_ZxQv"},"source":["and we trigger the generation process calling the `generate` method of the pipeline... We specify that we want `10` generations for each input"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":515,"referenced_widgets":["b689c54bb1584819a688a3a5ddadc244","136d4c25280e4568b2da72c383083809","8ae3a7625ea84b19a2addf4eb1ffb8cc","0b2eeb5c882e44ecb0b3b40ad9803f2a","4cb2d2df76eb473daff13135f955d793","c2eabf12fcbf4e75a5ef794ba1ad13e5","df72140b3c544c6a96a0579bfc2e1c73","64cfb92dacc546d68bb7b98e7752874d","88bfc7917f0941779a4d092debd586a4","10cbf7c3ab5f43b9a65073db593c6a5f","8f162bcb090f45e7bbbe7c7821f542bd","7f27083a093a4f43a613496689bf3f25","58cb8e9389cb4096af13a62b1365718a","d11504d1e86c444b9dbc11272288005d","6703f5738f904c97931b7988e15e46de","e4b04e0d97cf4058a0dc3523e09dd3fc","c21550505dc747b59cfa9cb6f613b36d","11b66141ac604485bbbeb2dc527fa955","a9788ac98de44a75953d1e4a79368493","838d5062812c4394852537ce6330e281","f98e13d7856d4b92b201fe13e0ad233d","b58e824d7f8d49c286d65282c8986a9a","9e91e2e8a54d4b9286c172751bda4210","e40f93919d4f4191bed73f84400b8841"]},"executionInfo":{"elapsed":133358,"status":"ok","timestamp":1701177852457,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"PtUfJ-ryzrcu","outputId":"06e717bd-3e4f-4163-80c7-8db5a7a0ef29"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b689c54bb1584819a688a3a5ddadc244","version_major":2,"version_minor":0},"text/plain":["Output()"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["INFO:distilabel:Processing batch 1 of 12...\n","INFO:distilabel:Calling generator for batch 1...\n","INFO:distilabel:Processing batch 2 of 12...\n","INFO:distilabel:Calling generator for batch 2...\n","INFO:distilabel:Processing batch 3 of 12...\n","INFO:distilabel:Calling generator for batch 3...\n","INFO:distilabel:Processing batch 4 of 12...\n","INFO:distilabel:Calling generator for batch 4...\n","INFO:distilabel:Processing batch 5 of 12...\n","INFO:distilabel:Calling generator for batch 5...\n","INFO:distilabel:Processing batch 6 of 12...\n","INFO:distilabel:Calling generator for batch 6...\n","INFO:distilabel:Processing batch 7 of 12...\n","INFO:distilabel:Calling generator for batch 7...\n","INFO:distilabel:Processing batch 8 of 12...\n","INFO:distilabel:Calling generator for batch 8...\n","INFO:distilabel:Processing batch 9 of 12...\n","INFO:distilabel:Calling generator for batch 9...\n","INFO:distilabel:Processing batch 10 of 12...\n","INFO:distilabel:Calling generator for batch 10...\n","INFO:distilabel:Processing batch 11 of 12...\n","INFO:distilabel:Calling generator for batch 11...\n","INFO:distilabel:Processing batch 12 of 12...\n","INFO:distilabel:Calling generator for batch 12...\n"]},{"data":{"text/html":["
\n"],"text/plain":[]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["
\n","
\n"],"text/plain":["\n"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8ae3a7625ea84b19a2addf4eb1ffb8cc","version_major":2,"version_minor":0},"text/plain":["Flattening the indices:   0%|          | 0/46 [00:00\n","\n"],"text/plain":["\n"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["time: 2min 13s (started: 2023-11-28 13:21:58 +00:00)\n"]}],"source":["distiset = pipeline.generate(\n","    dataset=dataset,\n","    num_generations=10,\n","    batch_size=4\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":532,"status":"ok","timestamp":1701178114392,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"g9occXMW0CS3","outputId":"bf70225b-9ee2-4885-9c81-47c7683e32c7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of generated instructions: 4637\n","time: 60.1 ms (started: 2023-11-28 13:28:33 +00:00)\n"]}],"source":["import re\n","\n","def transform(inst: str) -> str:\n","    \"\"\"Remove 1., 2., ... from the instruction.\"\"\"\n","    clean_inst = re.sub(r'^\\d+\\.\\s*', '', inst)\n","    return f\"{clean_inst}\"\n","\n","instructions = [\n","    transform(instruction)\n","    for generations in distiset[\"instructions\"]\n","    for generation in generations\n","    for instruction in generation\n","    if instruction != \"\"\n","]\n","print(f\"Number of generated instructions: {len(instructions)}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":226,"status":"ok","timestamp":1701182291830,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"HcHu_c4mbwOe","outputId":"98cc1456-65a1-45af-c3c8-828b501a1a71"},"outputs":[{"name":"stdout","output_type":"stream","text":["How can the concept of probability be applied in real-life scenarios? \n","Could you outline the process to solve a quadratic equation using the quadratic formula?\n","Explain the process of expanding the binomial expression (x + 3)^2 step by step.\n","How can I find the sum of an arithmetic series?\n","Explain the concept of factorial and provide an example of its application in real-life scenarios.\n","time: 8.4 ms (started: 2023-11-28 14:38:11 +00:00)\n"]}],"source":["import random\n","\n","samples = random.sample(instructions, 5)\n","\n","for sample in samples:\n","    print(sample)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":535,"status":"ok","timestamp":1701178118590,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"Sp7lbyfcdmiI","outputId":"aceced70-6477-41c2-e082-2277d60ae26d"},"outputs":[{"name":"stdout","output_type":"stream","text":["time: 17.8 ms (started: 2023-11-28 13:28:37 +00:00)\n"]}],"source":["dataset = Dataset.from_dict({\"instructions\": instructions})"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":131,"referenced_widgets":["a70c3be9900c41dea86c85490cb77c2d","4569748739b54af9a7747d5fa87e2144","7da0fbf826de4ef6b5ba7b05ab8a89cb","be5024ffd3474ebba98e64bedd61c099","9c7c379080e64c3a8f9a78aa30031f91","d4204267e89d461e8f062aff5cc8003f","ff45dfbcb201400d83fb8d22f80800a3","c59a9980b4f041d28b6a8c2d811f3d18","c3323dda5eca413480563bbb386054bd","f46b736d7dde48deae0513c5bfcd763e","f5ba3331c9cf4077952b081d21f27205","6db7a2e6384e4d44a9fc8c58cad4606a","8a8e63f88c0a44fb826f3cf8db36eff2","4e4bcea8214048ab8eef271845fdabb2","053d6f9c93d644f1ba0c691c66b470a6","48041d0944ab4f4687dcbbff7d43c214","b9be4144c87f4750908ac4463a6b4fa0","403b06d12fbb4d9ba02ae2f1ac5c50c8","529dcb50b22644f28cb7e4a8d3c104ef","8c02b949b1ef492d96be7f78efd3ee19","fff35ea38e8145c1be5cec4118dc0426","257de17ccdf444749f1abde950817562","f86181ec9ffa4ef29ee3b1c16415d93e","bb09b7b7c3a64a2d8946f22e317e2c22","9a44fdab9d2a45a69bdb451aec343f3b","7a0debb756874ac2be74b6886a101a77","a6df6936d7b84758ac0ed27bccde84a9","8ed252b97c1545f69b5041e720e4baf8","f21081c0e48149e6bd63a2cb8395788f","f3bd4b39e7ab4fc09bb45acd6e4bb4ce","cff570609e1245998eb9a86e1223a24f","d81431afdfb94a10a6ff349834c2f619","b43a4a0aef674280bf4d4a252b951161"]},"executionInfo":{"elapsed":1502,"status":"ok","timestamp":1700747738001,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"Y-i23xECc_J7","outputId":"6bf3f81b-fa02-40ff-cf7f-a94a38b18db2"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a70c3be9900c41dea86c85490cb77c2d","version_major":2,"version_minor":0},"text/plain":["Uploading the dataset shards:   0%|          | 0/1 [00:00\n"],"text/plain":[]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["
\n","
\n"],"text/plain":["\n"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"81a74705aa8147ee921accc40ff8e7c9","version_major":2,"version_minor":0},"text/plain":["Flattening the indices:   0%|          | 0/100 [00:00\n","\n"],"text/plain":["\n"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["time: 5min 8s (started: 2023-11-28 13:29:07 +00:00)\n"]}],"source":["distiset_pref = pipeline.generate(\n","    dataset=dataset.shuffle().select(range(100)),\n","    num_generations=3,\n","    batch_size=8\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1700757372181,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"spYhdTgrCdKG","outputId":"15dddde4-e998-43b2-9fce-1c1182ef6508"},"outputs":[{"data":{"text/plain":["['input',\n"," 'generation_model',\n"," 'generation_prompt',\n"," 'raw_generation_responses',\n"," 'generations',\n"," 'labelling_model',\n"," 'labelling_prompt',\n"," 'raw_labelling_response',\n"," 'rating',\n"," 'rationale']"]},"execution_count":42,"metadata":{},"output_type":"execute_result"},{"name":"stdout","output_type":"stream","text":["time: 4.16 ms (started: 2023-11-23 16:36:11 +00:00)\n"]}],"source":["distiset_pref.column_names"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":486,"status":"ok","timestamp":1700757486595,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"yHGUp0YBCuIV","outputId":"4f50039b-3aa6-404d-9eb0-6e1f081578f2"},"outputs":[{"name":"stdout","output_type":"stream","text":["Instruction: What is the significance of graph theory in computer science and network analysis?\n","Graph theory is a fundamental field in mathematics that has significant applications in computer science and network analysis. Here are a few ways it is significant:\n","\n","1. Network analysis: Graph theory provides a powerful framework for analyzing and understanding complex networks. It helps uncover key properties, such as connectivity, centrality, clustering, and community structure. These insights are essential for designing efficient and reliable networks, managing traffic, identifying vulnerabilities, and optimizing network performance.\n","\n","2. Data structures and algorithms: Graphs are commonly used as data structures in computer science. They are particularly useful for representing relationships and connections between entities. Many algorithms, such as Dijkstra's algorithm for shortest paths, the traveling salesman problem, and depth-first search, are based on graph theory and are widely employed in various computer science applications.\n","\n","3. Social networks and web analysis: Graph theory plays a crucial role in analyzing social networks and the structure of the web. It enables us to study patterns of interactions, identify influencers, detect communities, and understand information flow in complex systems like social media platforms and the World Wide Web.\n","\n","4. Circuit design and optimization: Graph theory aids in circuit design and optimization, especially in electronic and digital systems. By modeling circuits as graphs, engineers can analyze circuit behavior, optimize layouts, and solve various design problems that arise in areas like VLSI (Very Large Scale Integration) and hardware design.\n","\n","5. Computer graphics and image processing: Graph theory has applications in computer graphics and image processing. Techniques like graph coloring, graph matching, and graph-based segmentation are used for tasks such as color allocation, object recognition, and image compression.\n","\n","These are just a few examples of how graph theory is significant in computer science and network analysis. Its applications are vast and continue to grow as technology advances.\n","\n","\n","\n","Graph theory is highly significant in computer science and network analysis for a variety of reasons:\n","\n","1. Modeling Relationships: In computer science, graph theory allows us to model and represent complex relationships and dependencies between entities using graphs. This is particularly useful in network analysis, as it helps us understand the connections between nodes in a network, such as the internet or social networks.\n","\n","2. Algorithm Design: Graph algorithms play a crucial role in solving various computational problems efficiently. Many important algorithms, like Dijkstra's algorithm for finding the shortest path or Prim's algorithm for finding a minimum spanning tree, are based on graph theory. These algorithms are used extensively in network analysis, routing problems, optimizing resource allocation, and many other areas in computer science.\n","\n","3. Network Analysis: Graph theory provides a powerful framework for analyzing networks, both in theoretical and practical terms. It enables the identification of network structures, such as cliques, communities, or hubs, which helps in understanding network behavior, predicting patterns, and identifying vulnerabilities or bottlenecks.\n","\n","4. Optimization Problems: Graph theory offers tools for solving various optimization problems that arise in computer science and network analysis. For example, the traveling salesman problem, which seeks the shortest route through a set of cities, can be modeled as a graph problem, allowing the application of graph algorithms to find efficient solutions.\n","\n","5. Data Structures: Many data structures in computer science, such as trees and adjacency lists, are derived from graph theory. These data structures are fundamental for efficient storage and retrieval of information, and are widely used in implementing algorithms related to networks and other computational domains.\n","\n","Overall, graph theory provides a theoretical foundation and practical techniques to analyze and solve problems related to networks, computation, and data structures, making it an essential field within computer science and network analysis.\n","\n","\n","\n","Graph theory has significant applications in computer science and network analysis. Here are a few key areas where graph theory plays a crucial role:\n","\n","1. Network Analysis and Social Networks: Graph theory is fundamental to the analysis of complex networks, such as social networks, computer networks, biological networks, and transportation networks. It helps in understanding relationships, connectivity, and patterns within these networks.\n","\n","2. Data Structures and Algorithms: Many fundamental data structures and algorithms in computer science are based on graphs. Graphs are used to represent and model a variety of problems, including searching, sorting, shortest path algorithms, spanning trees, and flow optimization.\n","\n","3. Database Systems: Graph databases use graph theory to store and query data in a network-like structure. They enable efficient retrieval and analysis of interconnected data, making them suitable for domains like social media, recommendation systems, and fraud detection.\n","\n","4. Compiler Design and Code Optimization: Graph theory is employed in compiler design for optimizing code and representing dependencies between program components. Techniques like control flow graphs and data flow analysis utilize graph representations to optimize program execution.\n","\n","5. Artificial Intelligence and Machine Learning: Graphs are widely used in AI and machine learning to represent complex relationships and dependencies within data. Graph-based algorithms help in tasks like clustering, recommendation systems, community detection, and knowledge representation.\n","\n","6. Internet and Web Design: Web pages and links can be represented as graphs, enabling algorithms like PageRank to determine the importance and ranking of web pages. Graph theory is also used in studying internet topology, routing algorithms, and network flow optimization.\n","\n","Graph theory provides a powerful framework for modeling, analyzing, and solving problems in these areas and more. Its concepts and algorithms are essential tools for computer scientists and network analysts.\n","\n","\n","\n","time: 1.99 ms (started: 2023-11-23 16:38:05 +00:00)\n"]}],"source":["print(\"Instruction:\", distiset_pref[0][\"input\"])\n","for generation in distiset_pref[0][\"generations\"]:\n","    print(generation, end=\"\\n\\n\\n\\n\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":381,"status":"ok","timestamp":1700757542274,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"FXLA6xjUDANK","outputId":"4d61141d-27a0-4f29-e55e-f99d94859710"},"outputs":[{"data":{"text/plain":["['Text 1 fully aligns with the task goal and restrictions. It provides a comprehensive explanation of the significance of graph theory in computer science and network analysis by discussing multiple applications and how they are relevant in each area.',\n"," 'Text 2 almost fully aligns with the task goal and restrictions. It covers most of the significant ways graph theory is used in computer science and network analysis, but it could benefit from providing more specific examples or details in some areas.',\n"," 'Text 3 partially aligns with the task goal and restrictions. While it touches on some key areas where graph theory is significant, it lacks detailed explanations and examples in certain domains such as algorithm design and network analysis. It would benefit from providing a more comprehensive discussion in order to fully meet the requirements.']"]},"execution_count":51,"metadata":{},"output_type":"execute_result"},{"name":"stdout","output_type":"stream","text":["time: 4.56 ms (started: 2023-11-23 16:39:01 +00:00)\n"]}],"source":["distiset_pref[0][\"rationale\"]"]},{"cell_type":"markdown","metadata":{"id":"f8XNwxnM0ylP"},"source":["## Human Feedback with Argilla\n","\n","You can use the AI Feedback created by distilabel directly but we hae ve seen that enhancing it with human feedback will improve the quality of your LLM. We provide a `to_argilla` method which creates a dataset for Argilla along with out-of-the-box tailored metadata filters and semantic search to allow you to provide human feedback as quickly and engaging as possible. You can check [the Argilla docs](https://docs.argilla.io/en/latest/getting_started/quickstart_installation.html) to get it up and running.\n","\n","First, install it."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["!pip install \"distilabel[argilla]\""]},{"cell_type":"markdown","metadata":{},"source":["If you are running Argilla using the Docker quickstart image or Hugging Face Spaces, you need to init the Argilla client with the URL and API_KEY:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1871,"status":"ok","timestamp":1700753772131,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"3qgRZ9Ub3vG9","outputId":"80ae9bb2-831e-4284-a7b8-1d936bbf968a"},"outputs":[],"source":["import argilla as rg\n","\n","# Replace api_url with the url to your HF Spaces URL if using Spaces\n","# Replace api_key if you configured a custom API key\n","rg.init(\n","    api_url=\"http://localhost:6900\",\n","    api_key=\"owner.apikey\",\n","    workspace=\"admin\"\n",")"]},{"cell_type":"markdown","metadata":{},"source":["Now we can convert our dataset to a formatted Argilla dataset."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":414,"status":"ok","timestamp":1700753792169,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"BCjLWPSZyWov","outputId":"76503640-25cd-4b17-c95c-9fedf71d55a1"},"outputs":[{"name":"stdout","output_type":"stream","text":["time: 347 ms (started: 2023-11-23 15:36:31 +00:00)\n"]}],"source":["rg_dataset = distiset_pref.to_argilla()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8885,"status":"ok","timestamp":1700753820508,"user":{"displayName":"Gabriel Martรญn Blรกzquez","userId":"02784111684780831268"},"user_tz":-60},"id":"KaMCBrF3ymLp","outputId":"37a136e3-e4d4-4da7-b71f-4226a41b78c1"},"outputs":[{"data":{"text/plain":["RemoteFeedbackDataset(\n","   id=4232d7ac-eaff-49b0-88b8-3a384b76efbb\n","   name=math-preference-dataset\n","   workspace=Workspace(id=2fc2ebed-8d20-41b0-b33a-5c5f3712da53, name=admin, inserted_at=2023-11-23 15:00:40.160242, updated_at=2023-11-23 15:00:40.160242)\n","   url=https://gabrielmbmb-distilabel.hf.space/dataset/4232d7ac-eaff-49b0-88b8-3a384b76efbb/annotation-mode\n","   fields=[RemoteTextField(id=UUID('dc965a9c-ac85-449b-ae16-bda998c88c1c'), client=None, name='input', title='Input', required=True, type='text', use_markdown=False), RemoteTextField(id=UUID('0d29f518-a3bf-4642-a7d7-1324329555b7'), client=None, name='generations-1', title='Generations-1', required=True, type='text', use_markdown=False), RemoteTextField(id=UUID('c0541a45-8892-49fb-8b85-fcaa0cd147e0'), client=None, name='generations-2', title='Generations-2', required=True, type='text', use_markdown=False), RemoteTextField(id=UUID('4da8a4b5-553d-4b1d-a14f-b936a313797f'), client=None, name='generations-3', title='Generations-3', required=True, type='text', use_markdown=False)]\n","   questions=[RemoteRatingQuestion(id=UUID('fb012ff3-5fb7-40c0-b623-d4195c1508c8'), client=None, name='generations-1-rating', title=\"What's the rating for generations-1?\", description=None, required=True, type='rating', values=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), RemoteRatingQuestion(id=UUID('a7f02428-74e4-4497-8f26-803988e5c336'), client=None, name='generations-2-rating', title=\"What's the rating for generations-2?\", description=None, required=True, type='rating', values=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), RemoteRatingQuestion(id=UUID('cbab782f-b0c0-4ff6-8c64-c58ad3ea476a'), client=None, name='generations-3-rating', title=\"What's the rating for generations-3?\", description=None, required=True, type='rating', values=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), RemoteTextQuestion(id=UUID('405620c5-8aea-4fda-b92b-ee72e90629a9'), client=None, name='ratings-rationale', title=\"What's the rationale behind the ratings?\", description=None, required=True, type='text', use_markdown=False)]\n","   guidelines=None)"]},"execution_count":34,"metadata":{},"output_type":"execute_result"},{"name":"stdout","output_type":"stream","text":["time: 8.63 s (started: 2023-11-23 15:36:51 +00:00)\n"]}],"source":["rg_dataset.push_to_argilla(name=\"math-preference-dataset\", workspace=\"admin\")"]},{"cell_type":"markdown","metadata":{"id":"D-NDLF9606aX"},"source":["We can now jump in the UI and start providing human feedback to improve the quality of the synthetic dataset. 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diff --git a/docs/tutorials/pipeline-notus-instructions-preferences-legal.ipynb b/docs/tutorials/pipeline-notus-instructions-preferences-legal.ipynb
index 780f1219f8..a0734df147 100644
--- a/docs/tutorials/pipeline-notus-instructions-preferences-legal.ipynb
+++ b/docs/tutorials/pipeline-notus-instructions-preferences-legal.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# ๐Ÿค—ย Use Notus on inference endpoints to create a legal preference dataset\n",
+    "# โš–๏ธ Create a legal preference dataset\n",
     "\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/argilla-io/distilabel/blob/main/docs/tutorials/pipeline-notus-instructions-preferences-legal.ipynb) [![Open Source in Github](https://img.shields.io/badge/github-view%20source-black.svg)](https://github.com/argilla-io/distilabel/blob/main/docs/tutorials/pipeline-notus-instructions-preferences-legal.ipynb)\n",
     "\n",
@@ -40,36 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install argilla distilabel \"farm-haystack[preprocessing]\" pip install \"distilabel[hf-inference-endpoints]\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Running Argilla\n",
-    "\n",
-    "For this tutorial, you can use Argilla to visualize and annotate the different datasets created by distilabel. There are two main options for deploying and running Argilla:\n",
-    "\n",
-    "**Deploy Argilla on Hugging Face Spaces:** If you want to run tutorials with external notebooks (e.g., Google Colab) and you have an account on Hugging Face, you can deploy Argilla on Spaces with a few clicks:\n",
-    "\n",
-    "[![deploy on spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-to-spaces-lg.svg)](https://huggingface.co/new-space?template=argilla/argilla-template-space)\n",
-    "\n",
-    "For details about configuring your deployment, check the [official Hugging Face Hub guide](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla).\n",
-    "\n",
-    "**Launch Argilla using Argilla's quickstart Docker image**: This is the recommended option if you want [Argilla running on your local machine](../../getting_started/quickstart.ipynb). Note that this option will only let you run the tutorial locally and not with an external notebook service.\n",
-    "\n",
-    "For more information on deployment options, please check the Deployment section of the documentation.\n",
-    "\n",
-    "
\n",
-    "\n",
-    "Tip\n",
-    "\n",
-    "This tutorial is a Jupyter Notebook. There are two options to run it:\n",
-    "\n",
-    "- Use the Open in Colab button at the top of this page. This option allows you to run the notebook directly on Google Colab. Don't forget to change the runtime type to GPU for faster model training and inference.\n",
-    "- Download the .ipynb file by clicking on the View source link at the top of the page. This option allows you to download the notebook and run it on your local machine or on a Jupyter notebook tool of your choice.\n",
-    "
\n"
+    "%pip install  distilabel \"farm-haystack[preprocessing]\" pip install \"distilabel[hf-inference-endpoints]\""
    ]
   },
   {
@@ -90,8 +61,6 @@
     "import os\n",
     "from typing import Dict\n",
     "\n",
-    "import argilla as rg\n",
-    "\n",
     "from distilabel.llm import InferenceEndpointsLLM\n",
     "from distilabel.pipeline import Pipeline, pipeline\n",
     "from distilabel.tasks import TextGenerationTask, SelfInstructTask, Prompt\n",
@@ -104,28 +73,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you are running Argilla using the Docker quickstart image or Hugging Face Spaces, you need to init the Argilla client with the URL and API_KEY:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Replace api_url with the url to your HF Spaces URL if using Spaces\n",
-    "# Replace api_key if you configured a custom API key\n",
-    "rg.init(\n",
-    "    api_url=\"https://ignacioct-argilla.hf.space\",\n",
-    "    api_key=\"owner.apikey\",\n",
-    "    workspace=\"admin\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
+    "### Environment variables\n",
+    "\n",
     "Additionally, we need to provide our HuggingFace and OpenAI accest token. To later instatiate an `InferenceEndpointsLLM` object, we need to pass as parameters the HF Inference Endpoint name and the HF namespace. One very convenient way to do so is also through environment variables.\n"
    ]
   },
@@ -801,7 +750,50 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Upload the preference dataset to Argilla to annotate.\n",
+    "## Human Feedback with Argilla\n",
+    "\n",
+    "You can use the AI Feedback created by distilabel directly but we hae ve seen that enhancing it with human feedback will improve the quality of your LLM. We provide a `to_argilla` method which creates a dataset for Argilla along with out-of-the-box tailored metadata filters and semantic search to allow you to provide human feedback as quickly and engaging as possible. You can check [the Argilla docs](https://docs.argilla.io/en/latest/getting_started/quickstart_installation.html) to get it up and running.\n",
+    "\n",
+    "First, install it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install \"distilabel[argilla]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are running Argilla using the Docker quickstart image or Hugging Face Spaces, you need to init the Argilla client with the URL and API_KEY:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argilla as rg\n",
+    "\n",
+    "# Replace api_url with the url to your HF Spaces URL if using Spaces\n",
+    "# Replace api_key if you configured a custom API key\n",
+    "rg.init(\n",
+    "    api_url=\"http://localhost:6900\",\n",
+    "    api_key=\"owner.apikey\",\n",
+    "    workspace=\"admin\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "\n",
     "Once our preference dataset has been correctly generated, the Argilla UI is the best tool at our disposal to visualize it and annotate it. As for the instruction dataset, we just have to convert it to an Argilla Feedback Dataset, and push it to Argilla.\n"
    ]
diff --git a/mkdocs.yml b/mkdocs.yml
index 7dd70765e1..5f69b20c80 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -84,8 +84,9 @@ nav:
   - Getting started: index.md
   - Concepts: concepts.md
   - Tutorials:
-      - Use Notus on inference endpoints to create a legal preference dataset: tutorials/pipeline-notus-instructions-preferences-legal.ipynb
-      - Clean a Preference Dataset with the JudgeLMTask and GPT4-turbo: tutorials/clean-preference-dataset-judgelm-gpt.ipynb
+      - โš–๏ธ Create a legal preference dataset: tutorials/pipeline-notus-instructions-preferences-legal.ipynb
+      - ๐Ÿงผ Clean an existing preference dataset: tutorials/clean-preference-dataset-judgelm-gpt.ipynb
+      - ๐Ÿงฎ Create a mathematical preference dataset : tutorials/create-a-math-preference-dataset.ipynb
   - Technical References:
       - Concept Guides:
           - technical-reference/index.md