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: instruction
    dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 3853
    num_examples: 10
  download_size: 5213
  dataset_size: 3853
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-no-penalty-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-no-penalty-70B/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dvilasuero/test-outputs-no-penalty-70B/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "instruction": "How can I train a model with PyTorch on multiple GPUs using DataParallel?\n\nI\u0027m using PyTorch 1.9 and I have 8 Tesla V100 GPUs. I want to train a large model on multiple GPUs to speed up training. I\u0027m currently using the DataParallel module, but I\u0027ve heard that it\u0027s deprecated and may not be the best choice for my use case.\n\nDataParallel does work, but I\u0027m getting warnings about it being deprecated. What are the alternatives to DataParallel, and how do I use them to train my model on multiple GPUs?\n\nAlso, what\u0027s the difference between DataParallel and DistributedDataParallel, and when should I use each?\n\nI\u0027m using a custom dataset class and a custom model class. Here\u0027s some of my code:\n```python\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\n\nclass CustomDataset(Dataset):\n    def __init__(self, data, labels):\n        self.data = data\n        self.labels = labels\n\n    def __len__(self):\n        return len(self.data)\n\n    def __getitem__(self, idx):\n        return self.data[idx], self.labels[idx]\n\nclass CustomModel(nn.Module):\n    def __init__(self):\n        super(CustomModel, self).__init__()\n        self.fc1 = nn.Linear(784, 128)\n        self.fc2 = nn.Linear(128, 10)\n\n    def forward(self, x):\n        x = torch.relu(self.fc1(x))\n        x = self.fc2(x)\n        return x\n\n# Create dataset and data loader\ndataset = CustomDataset(data, labels)\nbatch_size = 128\ndata_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n\n# Create model and move to GPU\nmodel = CustomModel()\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\"\nmodel.to(device)\n\n# Create DataParallel model\nmodel = nn.DataParallel(model)\n\n# Train model\nfor epoch in range(10):\n    for batch in data_loader:\n        inputs, labels = batch\n        inputs, labels = inputs.to(device), labels.to(device)\n        optimizer.zero_grad()\n        outputs = model(inputs)\n        loss = nn.CrossEntropyLoss()(outputs, labels)\n        loss.backward()\n        optimizer.step()\n```\n\nHow can I modify my code to use the recommended approach for multi-GPU training in PyTorch?",
    "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/test-outputs-no-penalty-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-no-penalty-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},
}
```


