diff --git a/docs/tutorials/create-evol-instruct-dataset.ipynb b/docs/tutorials/create-evol-instruct-dataset.ipynb new file mode 100644 index 0000000000..f77b6fec90 --- /dev/null +++ b/docs/tutorials/create-evol-instruct-dataset.ipynb @@ -0,0 +1,616 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ๐Ÿง™ Create an evol-instruct 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/create-evol-instruct-dataset.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/create-evol-instruct-dataset.ipynb)\n", + "\n", + "In this tutorial, we'll develop an evol-instruct dataset by employing the approaches outlined in [\"WizardLM: Empowering Large Language Models to Follow Complex Instructions\"](https://arxiv.org/pdf/2304.12244.pdf) and [What makes good data for alignment? A comprehensive study of automatic data selection in instruction tuning](https://arxiv.org/pdf/2312.15685.pdf) using `distilabel`. In the next section, we will describe the process in detail. So, let's get started! ๐Ÿช„" + ] + }, + { + "attachments": { + "image-2.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The WizardLM paper proposes a new method, **Evol-Instruct**, to synthetically create a dataset with open-domain instructions of varying complexity using *gpt-3.5-turbo*. The resulting dataset, combined with the original, was used to fine-tune LLaMa, leading to the creation of WizardLM. This model surpasses ChatGPT in both human and automatic evaluations, demonstrating more than 90% of ChatGPT's capabilities in 17 out of 29 skills.\n", + "\n", + "In this tutorial, we will only focus on the *Evol-Instruct* approach to create a more complex dataset. From an *initial dataset* that will be the seed for the evolution process, the steps for each epoch (determined as M=4) are as follows:\n", + "\n", + "1. **Intruction Evolving**: Use *gpt-3.5-turbo* with predefined prompts to generate the evolved instructions. These prompts can be of two types: *in-depth evolving* (includes adding constraints, deepening, concretizing, increasing reasoning, and complicating the input) and *in-breadth evolving* (includes mutation). The complicating prompt is the only one not applied as it needs in-context examples. Then, only one of the remaining five is selected randomly to be applied to the input instruction. You can check the original code [here](https://github.com/nlpxucan/WizardLM/tree/main/Evol_Instruct).\n", + "2. **Elimination Evolving**\n", + " * The instruction evolving step may fail, so the new instructions are filtered according to the following criteria:\n", + " 1. The evolved instruction *does not provide any information* gain. Automatically evaluated with ChatGPT.\n", + " 2. The evolved instruction contains *\"sorry\" and is less than 80 words*.\n", + " 3. The evolved instruction only contains *punctuation and stop words*.\n", + " 4. The evolved instruction *copies words* from the evolving prompt.\n", + " * If the evolved instruction passes the previous criteria, it is added to the pool of new instructions and also will be used as input for the next iteration. If not, it is dropped and the original instruction is the one used for the next iteration.\n", + "\n", + "Once, the evolved instructions are generated, they use the same LLM to **generate the corresponding responses**. Finally, the resulting dataset is the combination of the original and the new instructions generated in each epoch.\n", + "\n", + "![image-2.png](attachment:image-2.png)\n", + "\n", + "On the other hand, the Deita paper proposes more strategies to select the best data for alignment. While using the *Evol-Instruct* approach, but without the breadth evolving step, what they called **Evol-Complexity**. They also applied the **Evol-quality** and **Data selection** strategies.\n", + "\n", + "* The **Evol-quality** is similar to Evol-Complexity, although it uses a different prompt, which is focused on improving the quality of the responses by enhancing helpfulness, augmenting relevance, enriching depth, fostering creativity, and supplying additional details, to generate new pairs.\n", + "* The **Data Selection** strategy filters the new instructions using embeddings and cosine similarity to the original instructions to select the best and most diverse ones.\n", + "\n", + "In the next sections, we will see how to use these approaches to build our dataset using `distilabel`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting started" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install dependencies\n", + "\n", + "Letโ€™s start by installing the required dependencies to run *distilabel*. You can also install argilla for better visualization and curation of the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q -U \"distilabel[openai,argilla]\" --upgrade" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can import the required libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import string\n", + "import time\n", + "from dataclasses import dataclass\n", + "from typing import Dict, List\n", + "\n", + "import pandas as pd\n", + "from datasets import Dataset, load_dataset\n", + "\n", + "from distilabel.dataset import CustomDataset\n", + "from distilabel.llm import LLM, OpenAILLM\n", + "from distilabel.pipeline import Pipeline\n", + "from distilabel.tasks import EvolComplexityTask, Prompt, EvolQualityTask, TextGenerationTask" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the OpenAI API Key\n", + "os.environ[\"OPENAI_API_KEY\"] = 'sk-...'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare the initial dataset\n", + "\n", + "The first step is to prepare the initial dataset that will be used for the evolution process. Following the same idea as shown in an example from the paper, we will use the well-known [alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) dataset available in HuggingFace. For the sake of this tutorial's example, we will use 5 samples.\n", + "\n", + "Good to mention that other datasets like the [distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs), a \"distilabeled\" version of orca_dpo_pairs for preference tuning with 12.9K samples, were also applied as the seed dataset. However, the instructions were already too complex, so the evolution process generated a small amount of instructions that were of poor-quality or with hallucinations." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the dataset\n", + "hf_dataset = load_dataset(\"tatsu-lab/alpaca\", split=\"train\")\n", + "\n", + "# Get our initial dataset\n", + "initial_dataset = (\n", + " hf_dataset\n", + " .select_columns([\"instruction\", \"output\"])\n", + " .rename_column(\"instruction\", \"input\")\n", + " .rename_column(\"output\", \"response\")\n", + ")\n", + "\n", + "# Select a subset\n", + "initial_dataset = initial_dataset.shuffle(seed=5).select(range(5))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input': 'Generate a list of three ingredients for a chocolate cake.',\n", + " 'response': '- Flour\\n- Cocoa powder\\n- Sugar'}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "initial_dataset[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The `Evol-Complexity` approach\n", + "\n", + "For our case, we will need to set two different LLMs with their corresponding tasks: one for the instruction evolving and another for the elimination evolving step 1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instruction Evolving LLM\n", + "\n", + "The next step is to define the LLM that will be used to generate the evolved instructions. We will use *gpt-3.5-turbo* as the language model, and the task `EvolComplexityTask`, also we will set some parameters (Section 4.3 from WizardLM). Take into account that the `EvolComplexity` will perform the random selection of the evolving prompt and the filtering of the evolved instructions up the first step from the elimination evolving related to *equal prompts*." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Define our LLM\n", + "complexity_llm = OpenAILLM(\n", + " task=EvolComplexityTask(),\n", + " api_key=os.getenv(\"OPENAI_API_KEY\"),\n", + " model= \"gpt-3.5-turbo\",\n", + " num_threads=4,\n", + " max_new_tokens=2048,\n", + " temperature=1,\n", + " frequency_penalty=0.0,\n", + " top_p=0.9,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Elimination Evolving LLM\n", + "\n", + "As part of the elimination step, it was stated to ask ChatGPT if the original prompt and the evolved one from the current epoch are equal. In order to do so, we will need to define a LLM with the corresponding task. As the task does not exist, we will customize one based on `TextGenerationTask` from `distilabel` indicating how to generate the prompt and parse the output." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Indicate the prompt (Appendix G from WizardLM)\n", + "elimination_equal_prompt = \"\"\"Here are two Instructions, do you think they are equal to each other and meet the following requirements?:\n", + " 1. They have the same constraints and requirements.\n", + " 2. They have the same depth and breadth of the inquiry.\n", + " The First Prompt: {first_instruction}\n", + " The Second Prompt: {second_instruction}\n", + " Your Judgement (Just answer: Equal or Not Equal. No need to explain the reason):\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Define our distilabel class\n", + "@dataclass\n", + "class EliminationEqualPrompts(TextGenerationTask):\n", + "\n", + " system_prompt: str = \"You are an AI judge in charge of determining the equality of two instructions. \"\n", + "\n", + " def generate_prompt(self, input: List[str]) -> Prompt:\n", + " return Prompt(\n", + " system_prompt=self.system_prompt,\n", + " formatted_prompt=elimination_equal_prompt.format(\n", + " first_instruction=input[0], second_instruction=input[1]\n", + " ),\n", + " )\n", + "\n", + " def parse_output(self, output: str) -> List[Dict[str, str]]:\n", + " \"\"\"Remove punctuation from the string and lowercase it.\"\"\"\n", + " return {\n", + " \"generations\": output.translate(\n", + " str.maketrans(\"\", \"\", string.punctuation)).lower()\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use this task in our LLM definition. Similarly to the paper, the parameters will be the same as the ones used in the previous section." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Define out second LLM\n", + "elimination_llm = OpenAILLM(\n", + " task=EliminationEqualPrompts(),\n", + " api_key=os.getenv(\"OPENAI_API_KEY\"),\n", + " model= \"gpt-3.5-turbo\",\n", + " num_threads=4,\n", + " max_new_tokens=2048,\n", + " temperature=1,\n", + " frequency_penalty=0.0,\n", + " top_p=0.9,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The `Evol-quality` approach\n", + "\n", + "Following the Deita paper idea, we will run the `Evol-quality` approach to generate new responses from those generated instructions in the previous section focusing on quality. Similarly, we will define the LLM and the `EvolQualityTask` to generate the new responses." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Define our LLM\n", + "quality_llm = OpenAILLM(\n", + " task=EvolQualityTask(),\n", + " api_key=os.getenv(\"OPENAI_API_KEY\"),\n", + " model= \"gpt-4-turbo-preview\",\n", + " num_threads=4,\n", + " max_new_tokens=2048,\n", + " temperature=1,\n", + " frequency_penalty=0.0,\n", + " top_p=0.9,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the evolution process" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To run the evolution process, we will create the `make_evol_instruct_dataset` function that will take the defined LLMs, the initial dataset, and the number of evolution steps. In our approach, we will follow the steps from WizardLM, but using the Evol-Complexity task and their number of epochs, as well as Evol-Quality. To clarify, for each complexity step, we followed this process:\n", + "\n", + "* Run the complexity pipe to generate new instructions from the previous ones. Deita: *For each instruction sample $I^{(0)}_k$, we use the In-Depth Evolving Prompt [...]. After $M$ iterations, we obtain a set of instructions across different complexities for $I_k$, $\\{I^{(0)}_k, \\ldots, I^{(M)}_k\\}$.*\n", + "* Execute the elimination pipe to filter the new instructions. WizardLM: *The evolved instruction does not provide any information gain. Automatically evaluated with ChatGPT.*\n", + "* Create inside the current epoch a loop to generate the new responses for each new successful instruction. The generated samples will be saved for the final dataset. Deita: *After $M$ iterations, for the same instruction $I^{(0)}_k$, we procure a set of responses spanning various qualities for $R_k$, denoted as $\\{R^{(0)}_k, \\ldots, R^{(M)}_k\\}$*.\n", + "* The input for the next complexity step will be the successful instructions with their associated initial responses." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper functions to generate the evol-instruct dataset\n", + "def prepare_for_equal_prompts(example):\n", + " \"\"\"\"If the evolved instruction is None, we use the original instruction (to make sure it will be removed)\"\"\"\n", + " if example[\"instructions\"][0] is None:\n", + " return {\"input\": [example[\"input\"], example[\"input\"]]}\n", + " else:\n", + " return {\"input\": [example[\"input\"], example[\"instructions\"][0]]}\n", + " \n", + "def prepare_for_evol_quality(example):\n", + " return {\"input\": example[\"instructions\"][0], \"generation\": example[\"response\"]}" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "def make_evol_instruct_dataset(\n", + " complexity_llm: LLM, \n", + " elimination_llm: LLM,\n", + " quality_llm: LLM,\n", + " dataset: Dataset,\n", + " instruction_steps: int = 4,\n", + " responses_steps: int = 4\n", + " ) -> \"Dataset\":\n", + " \n", + " # Set the pipelines\n", + " complexity_pipe = Pipeline(generator=complexity_llm)\n", + " elimination_pipe = Pipeline(generator=elimination_llm)\n", + " quality_pipe = Pipeline(generator=quality_llm)\n", + " \n", + " # Set the initial dataset\n", + " input_complexity = dataset\n", + " successful_samples = []\n", + "\n", + " # Start the evolution process\n", + " for step in range(1, instruction_steps + 1):\n", + " print(f\"Evolving instruction step: {step}/{instruction_steps}\")\n", + "\n", + " # Run the complexity pipe to generate new instructions\n", + " instruction_dataset = complexity_pipe.generate(input_complexity, batch_size=8)\n", + "\n", + " # Run the elimination pipe to determine if the instructions are equal\n", + " prepared_dataset = (\n", + " instruction_dataset\n", + " .map(prepare_for_equal_prompts)\n", + " .select_columns([\"input\"])\n", + " )\n", + " elimination_dataset=elimination_pipe.generate(prepared_dataset, batch_size=8)\n", + " \n", + " # Save the successful instructions to be used for quality evol and prepare the inputs for the next iteration\n", + " new_instructions = []\n", + " responses= []\n", + " successful_instructions = []\n", + " \n", + " for row_evolved, row_elimination in zip(instruction_dataset, elimination_dataset):\n", + " if (row_evolved['instructions'][0] is not None) and (row_elimination['generations'][0] != \"equal\"):\n", + " new_instructions.append(row_evolved['instructions'][0])\n", + " responses.append(row_evolved['response'])\n", + " successful_instructions.append(row_evolved)\n", + " else:\n", + " new_instructions.append(row_evolved['input'])\n", + " responses.append(row_evolved['response'])\n", + " \n", + " input_complexity = Dataset.from_dict({\"input\": new_instructions, \"response\": responses})\n", + " \n", + " # Run the quality pipe to generate new responses\n", + " complexity_dataset = pd.DataFrame(successful_instructions)\n", + " input_quality = Dataset.from_pandas(complexity_dataset).map(prepare_for_evol_quality).select_columns([\"input\", \"generation\"])\n", + " \n", + " for q_step in range(1, responses_steps + 1):\n", + " print(f\"Evolving response step: {q_step}/{responses_steps}\")\n", + "\n", + " # Generate new responses\n", + " response_dataset = quality_pipe.generate(input_quality, batch_size=8)\n", + " \n", + " # Save the successful responses in the pool and prepare the inputs for the next iteration\n", + " inputs = []\n", + " new_responses = []\n", + " \n", + " for row in response_dataset:\n", + " inputs.append(row['input'])\n", + " new_responses.append(row['generations'][0])\n", + " successful_samples.append(row)\n", + " \n", + " input_quality = Dataset.from_dict({\"input\": inputs, \"generation\": new_responses})\n", + "\n", + " # Prepare the final dataset\n", + " df_final_dataset = pd.DataFrame(successful_samples)\n", + " final_dataset = Dataset.from_pandas(df_final_dataset)\n", + " final_dataset.__class__ = CustomDataset\n", + " final_dataset.task = TextGenerationTask() #or EvolQualityTask()\n", + " \n", + " return final_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, let's make our first evol-instruct dataset! ๐Ÿง™" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds_evol_instruct = make_evol_instruct_dataset(\n", + " complexity_llm=complexity_llm,\n", + " elimination_llm=elimination_llm,\n", + " quality_llm=quality_llm,\n", + " dataset=initial_dataset,\n", + " instruction_steps=5,\n", + " responses_steps=5,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds_evol_instruct" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input': 'Provide a selection of three specific ingredients for a decadent dark chocolate raspberry cake.',\n", + " 'generation': '- Flour\\n- Cocoa powder\\n- Sugar',\n", + " 'generation_model': ['gpt-4-turbo-preview'],\n", + " 'generation_prompt': [[{'content': '', 'role': 'system'},\n", + " {'content': \"I want you to act as a Response Rewriter\\nYour goal is to enhance the quality of the response given by an AI assistant\\nto the #Given Prompt# through rewriting.\\nBut the rewritten response must be reasonable and must be understood by humans.\\nYour rewriting cannot omit the non-text parts such as the table and code in\\n#Given Prompt# and #Given Response#. Also, please do not omit the input\\nin #Given Prompt#.\\nYou Should enhance the quality of the response using the following method:\\nPlease make the Response more in-depth.\\nYou should try your best not to make the #Rewritten Response# become verbose,\\n#Rewritten Response# can only add 10 to 20 words into #Given Response#.\\n'#Given Response#', '#Rewritten Response#', 'given response' and 'rewritten response'\\nare not allowed to appear in #Rewritten Response#\\n#Given Prompt#:\\nProvide a selection of three specific ingredients for a decadent dark chocolate raspberry cake.\\n#Given Response#:\\n- Flour\\n- Cocoa powder\\n- Sugar\\n#Rewritten Response#:\",\n", + " 'role': 'user'}]],\n", + " 'raw_generation_responses': ['- High-quality all-purpose flour\\n- Unsweetened dark cocoa powder\\n- Granulated white sugar'],\n", + " 'generations': ['- High-quality all-purpose flour\\n- Unsweetened dark cocoa powder\\n- Granulated white sugar']}" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_evol_instruct[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optionally, we can push the dataset to HuggingFace to share it with the community thanks to the `push_to_hub` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Push to Hugging Face\n", + "HF_REPO_ID = \"argilla/distilabel-evol-instruct-dataset\"\n", + "ds_evol_instruct.push_to_hub(\n", + " HF_REPO_ID, # type: ignore\n", + " split=\"train\",\n", + " private=False,\n", + " token=os.getenv(\"HF_TOKEN\", None),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Human Feedback with Argilla\n", + "\n", + "You can use the AI Feedback created by distilabel directly but we have seen that enhancing it with human feedback will improve the quality of your LLM. So, 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": [ + "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=\"argilla.apikey\",\n", + " workspace=\"argilla\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now push the dataset to Argilla as follows and curate even more the evolved instructions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the dataset to Argilla format adding questions and metadata\n", + "rg_dataset = ds_evol_instruct.to_argilla(vector_strategy=False, metric_strategy=False)\n", + "\n", + "# Push the dataset to Argilla\n", + "remote_rg_dataset = rg_dataset.push_to_argilla(name=\"distilabel-evol-instructions\", workspace=\"argilla\")" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusions\n", + "\n", + "In this tutorial, we followed our own approach for the methods from WizardLM and Deita to develop an evolved-instruction dataset. Using `distilabel`, we generated and evaluated new instructions, creating a dataset featuring successful instructions after applying Evol-Complexity to make them more complex and Evol-Quality to improve the quality of their responses. Optionally, we employed Argilla to verify their quality using human feedback.\n", + "\n", + "We hope you found this tutorial helpful! ๐Ÿ‘\n", + "\n", + "Explore different ways to create new datasets by checking out these tutorials!\n", + "\n", + "* [Clean an existing preference dataset](https://distilabel.argilla.io/latest/tutorials/clean-preference-dataset-judgelm-gpt.html)\n", + "* [Create a mathematical preference dataset](https://distilabel.argilla.io/latest/tutorials/create-a-math-preference-dataset.html)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/mkdocs.yml b/mkdocs.yml index b878c75658..94c0f1a30a 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -88,6 +88,7 @@ nav: - 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 - ๐Ÿฆ’ Improving Text Embeddings with LLMs : tutorials/improving-text-embeddings-with-llms.ipynb + - ๐Ÿง™ Create an evol-instruct dataset : tutorials/create-evol-instruct-dataset.ipynb - Technical References: - Concept Guides: - technical-reference/index.md