size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel arxiv:2406.08464 region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: completion
    dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 12142
    num_examples: 5
  download_size: 20740
  dataset_size: 12142
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for test-outputs-70B

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/dvilasuero/test-outputs-70B/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dvilasuero/test-outputs-70B/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "completion": "**Corrected Code with Comments for Clarity**\n\nThe provided code is already quite clear, but I\u0027ll add some additional comments to explain the thought process behind it.\n\n```python\nimport numpy as np\nfrom scipy.integrate import odeint\nimport matplotlib.pyplot as plt\n\n# Define the function that represents the right-hand side of the differential equation\n# dy/dt = [dy1/dt, dy2/dt] where y1(t) = y(t) and y2(t) = y\u0027(t)\ndef model(y, t):\n    # y[0] represents y(t) and y[1] represents y\u0027(t)\n    # The differential equation is y\u0027\u0027(t) + 2y\u0027(t) + 2y(t) = 0\n    # So, dy1/dt = y\u0027(t) and dy2/dt = -2y\u0027(t) - 2y(t)\n    dydt = [y[1], -2 * y[1] - 2 * y[0]] \n    return dydt\n\n# Define the initial condition\n# We are given that y(0) = 1 and y\u0027(0) = -1\ny0 = [1, -1]\n\n# Generate an array of time points from 0 to 10\nt = np.linspace(0, 10)\n\n# Solve the ordinary differential equation\n# odeint solves a system of ordinary differential equations\n# It takes in the function that represents the right-hand side of the equation,\n# the initial condition, and the time points at which we want the solution\ny = odeint(model, y0, t)\n\n# Plot the results\nplt.plot(t, y[:, 0], \u0027b\u0027, label=\u0027y(t)\u0027)\nplt.plot(t, y[:, 1], \u0027g\u0027, label=\"y\u0027(t)\")\nplt.legend(loc=\u0027best\u0027)\nplt.xlabel(\u0027t\u0027)\nplt.grid()\nplt.show()\n```\n\nThis code correctly solves the given differential equation using the `scipy.integrate.odeint` function and plots the solution.",
    "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct",
    "prompt": "**Numerical Solution of a Second-Order Differential Equation**\nThe differential equation is given as:\ny\u0027\u0027(t) + 2y\u0027(t) + 2y(t) = 0, y(0) = 1, y\u0027(0) = -1\n\nUse Python with the `scipy.integrate.odeint` function to find the solution and plot the result.\n\n```python\nimport numpy as np\nfrom scipy.integrate import odeint\nimport matplotlib.pyplot as plt\n\n# function that returns dy/dt\ndef model(y, t):\n    dydt = [y[1], -2 * y[1] - 2 * y[0]]\n    return dydt\n\n# initial condition\ny0 = [1, -1]\n\n# time points\nt = np.linspace(0, 10)\n\n# solve ODE\ny = odeint(model, y0, t)\n\n# plot results\nplt.plot(t, y[:, 0], \u0027b\u0027, label=\u0027y(t)\u0027)\nplt.plot(t, y[:, 1], \u0027g\u0027, label=\"y\u0027(t)\")\nplt.legend(loc=\u0027best\u0027)\nplt.xlabel(\u0027t\u0027)\nplt.grid()\nplt.show()\n```\n\n**Explanation:**\n\nThe above code is a solution to the differential equation y\u0027\u0027(t) + 2y\u0027(t) + 2y(t) = 0 with initial conditions y(0) = 1 and y\u0027(0) = -1. We first import the necessary libraries including numpy and scipy.integrate for numerical operations and odeint for solving ordinary differential equations. We then define a function `model` that represents the differential equation, where `y[0]` is y(t) and `y[1]` is y\u0027(t). The initial conditions are defined as `y0`. We then generate an array of time points `t` using `np.linspace` and solve the ODE using `odeint`. The result is then plotted.\n\nHowever, the above code doesn\u0027t fully match the differential equation given. Let\u0027s rewrite it to match the equation. The corrected code is:\n\n```python\nimport numpy as np\nfrom scipy.integrate import odeint\nimport matplotlib.pyplot as plt\n\n# function that returns dy/dt\ndef model(y, t):\n    dydt = [y[1], -2 * y[1] - 2 * y[0]] # corrected to match the equation\n    return dydt\n\n# initial condition\ny0 = [1, -1]\n\n# time points\nt = np.linspace(0, 10)\n\n# solve ODE\ny = odeint(model, y0, t)\n\n# plot results\nplt.plot(t, y[:, 0], \u0027b\u0027, label=\u0027y(t)\u0027)\nplt.plot(t, y[:, 1], \u0027g\u0027, label=\"y\u0027(t)\")\nplt.legend(loc=\u0027best\u0027)\nplt.xlabel(\u0027t\u0027)\nplt.grid()\nplt.show()\n```\n\nThe code is now corrected and it should work as expected."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/test-outputs-70B", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/test-outputs-70B")
```


</details>





## References


```
@misc{xu2024magpiealignmentdatasynthesis,
    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
    year={2024},
    eprint={2406.08464},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2406.08464},
}
```


