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: 14562
    num_examples: 5
  download_size: 8733
  dataset_size: 14562
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

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/raw/main/pipeline.yaml"
```

or explore the configuration:

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

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "instruction": "I\u0027m working on a deep learning model using PyTorch and I\u0027m experiencing issues with memory issues. I\u0027ve tried reducing the model size and batch size, but the memory usage is still too high. What are some in-depth suggestions for optimizing memory usage in PyTorch?\n\n### Potential suggestions:\n\n1.  **Model Pruning**: Pruning is a technique used to remove unnecessary connections from your neural network. It can significantly reduce the size of your model and alleviate memory usage in PyTorch.\n\n    *   ** Magnification**: Use the `torch.nn.utils.prune.l1_unstructured` to prune your model. You can use the following code to prune a model. Here\u0027s a basic example of how to use this:\n        ```\n        import torch\n        import torch.nn as nn\n        import torch.nn.utils.prune as prune\n\n        class Net(nn.Module):\n            def __init__(self):\n                super(Net, self).__init__()\n                self.fc1 = nn.Linear(5, 5)\n                self.fc2 = nn.Linear(5, 5)\n\n            def forward(self, x):\n                x = torch.relu(self.fc1(x))\n                x = self.fc2(x)\n                return x\n\n        model = Net()\n        pr = prune.l1_unstructured(model, \u0027weight\u0027, amount=0.3)\n        ```\n\n2.  **Knowledge Distillation**: Knowledge distillation is another technique for reducing the size of a model. It works by training a smaller network to imitate the behavior of a larger, more complex network.\n\n    *   **Magnification**: Use the following to implement knowledge distillation:\n        ```\n        from torch.utils.data import Dataset\n        from torch.utils.data import DataLoader\n        import torch.nn as nn\n        import torch.optim as optim\n        import torch.nn.functional as F\n\n        class Student(nn.Module):\n            def __init__(self):\n                super(Student, self).__init__()\n                self.fc1 = nn.Linear(5, 5)\n                self.fc2 = nn.Linear(5, 5)\n\n            def forward(self, x):\n                x = torch.relu(self.fc1(x))\n                x = self.fc2(x)\n                return x\n\n        class Teacher(nn.Module):\n            def __init__(self):\n                super(Teacher, self).__init__()\n                self.fc1 = nn.Linear(5, 5)\n                self.fc2 = nn.Linear(5, 5)\n\n            def forward(self, x):\n                x = torch.relu(self.fc1(x))\n                x = self.fc2(x)\n                return x\n\n        student = Student()\n        teacher = Teacher()\n\n        # Create a data loader\n        dataset = Dataset()\n        data_loader = DataLoader(dataset, batch_size=32, shuffle=True)\n\n        # Training loop\n        for epoch in range(10):\n            for i, (x, y) in enumerate(data_loader):\n                # Distillation loss\n                student_output = student(x)\n                teacher_output = teacher(x)\n                distillation_loss = F.kl_div(student_output, teacher_output)\n\n                # Regularizer\n                regularizer = 0.1 * F.l1_loss(student.fc1.weight, teacher.fc1.weight)\n\n                # Total loss\n                loss = distillation_loss + regularizer\n\n                # Optimization\n                optimizer = optim.Adam(student.parameters(), lr=0.001)\n                optimizer.zero_grad()\n                loss.backward()\n                optimizer.step()\n        ```\n\n3.  **Weight Quantization**: Weight quantization is a technique for reducing the size of your model by converting model weights from floating-point numbers to 8-bit integers.\n\n    *   **Magnification**: Use the following to implement weight quantization:\n        ```\n        import torch\n        import torch.nn as nn\n\n        model = nn.Linear(5, 5)\n        # Quantize the model\n        model_q = torch.quantization.quantize_dynamic(model)\n        ```\n\n4.  **Optimizing Data Loading**: Data loading is generally a common place to optimize for memory.  You can optimize the data loading process for memory usage by using the following techniques:\n\n    *   **Batching Data**: Use batches to reduce the memory usage. Here\u0027s an example of batch creation using PyTorch:\n        ```\n        import torch\n        import torch.nn as nn\n\n        # Load your data\n        data = torch.randn(1000, 5)\n\n        # Create a data loader with batch size\n        batch_size = 32\n        data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True)\n        ```\n\n5.  **Sampling**: You can use sampling techniques to optimize the data loading. Here\u0027s an example of sampling to optimize:\n    *   **Stratified Sampling**: The stratified sampling method ensures each batch has a representative sample from the class.\n        ```\n        import torch\n        from torch.utils.data import Subset, DataLoader\n        import numpy as np\n\n        # Load your data\n        data = torch.randn(1000, 5)\n\n        # Define sampling strategy\n        class StratifiedSampler(torch.utils.data.sampler.Sampler):\n            def __init__(self, data, batch_size):\n                self.data = data\n                self.batch_size = batch_size\n\n            def __iter__(self):\n                labels, counts = torch.unique(self.data[:, -1], return_counts=True)\n                labels = labels.tolist()\n                counts = counts.tolist()\n                batches = []\n                for i in range(len(labels)):\n                    batch = None\n                    label_count = 0\n                    for j in range(self.batch_size):\n                        index = None\n                        while index == index:\n                            index = np.random.randint(0, len(self.data))\n                            if self.data[index, -1] == labels[i] and label_count \u003c counts[i]:\n                                label_count += 1\n                                batch.append(index)\n                            else:\n                                pass\n                        if batch is None:\n                            batch = [index]\n                        else:\n                            batch.append(index)\n                    batches.append(batch)\n                for batch in batches:\n                    yield batch\n\n            def __len__(self):\n                return len(batches)\n\n        batch_size = 32\n        data_loader = DataLoader(data, batch_size=batch_size, sampler=StratifiedSampler(data, batch_size=batch_size))\n        ```\n\n6.  **Data Type**: Reducing the precision of your data can also help reduce memory usage. Here\u0027s an example of reducing data type to float32 from float64:\n    *   **Change Data Type**: Use the `dtype` parameter while creating a tensor to change the data type. Here\u0027s how you can do it:\n        ```\n        import torch\n\n        # Load your data\n        data = torch.tensor(np.random.rand(1000, 5), dtype=torch.float64)\n\n        # Change the data type to float32\n        data = data.float()\n        ```\n7.  **Using torch.Storage**: PyTorch\u0027s `torch.Storage` class can store tensors in a more memory-efficient way. Here\u0027s an example of using `torch.Storage` to store tensor:\n    *   **torch.Storage**: Use the following code to store a tensor in `torch.Storage` format:\n        ```\n        import torch\n\n        # Load your data\n        data = torch.randn(1000, 5)\n\n        # Store the data in torch.Storage\n        storage = data.storage()\n        ```\n\n8.  **Gradient Accumulation**: Gradient accumulation is a technique where you accumulate the gradients from multiple iterations and optimize the model once. Here\u0027s an example of gradient accumulation:\n    *   **Gradient Accumulation**: Use the following code to implement gradient accumulation:\n        ```\n        import torch\n        import torch.nn as nn\n        import torch.optim as optim\n\n        # Create a model and optimizer\n        model = nn.Linear(5, 5)\n        optimizer = optim.Adam(model.parameters(), lr=0.001)\n\n        # Set the number of iterations to accumulate\n        accumulation_steps = 4\n\n        # Set the gradient accumulation flag to True and optimizer\u0027s step count to 0\n        optimizer.zero_grad()\n        step = 0\n\n        # Training loop\n        for i in range(10):\n            # Forward pass\n            output = model(input)\n            loss = F.mse_loss(output, label)\n            loss.backward()\n\n            # Accumulate the gradient\n            step += 1\n            if step == accumulation_steps:\n                # Optimize the model\n                optimizer.step()\n                step = 0\n                optimizer.zero_grad()\n            else:\n                pass\n        ```\n9.  **Mixed Precision Training**: Training a model in mixed precision can also help reduce memory usage. Here\u0027s an example of mixed precision training:\n    *   **Mixed Precision Training**: Use the following code to implement mixed precision in PyTorch:\n        ```\n        import torch\n        import torch.nn as nn\n        import torch.optim as optim\n\n        # Create a model\n        model = nn.Linear(5, 5)\n\n        # Set the mixed precision flag\n        torch.cuda.amp.autocast(enabled=True)\n\n        # Create a model optimizer\n        optimizer = optim.Adam(model.parameters(), lr=0.001)\n\n        # Create a data loader\n        data_loader = torch.utils.data.DataLoader(torch.randn(100, 5), batch_size=10, shuffle=True)\n\n        # Training loop\n        for epoch in range(10):\n            for i, (x, y) in enumerate(data_loader):\n                # Cast to mixed precision\n                with torch.cuda.amp.autocast():\n                    output = model(x)\n                    loss = F.mse_loss(output, y)\n\n                # Backward pass\n                optimizer.zero_grad()\n                loss.backward()\n\n                # Optimization\n                optimizer.step()\n        ```\n10. **Static Graph**: PyTorch provides the ability to convert models into static graphs that can be executed on the GPU. Here\u0027s an example of converting a model to a static graph:\n    *   **Static Graph**: Use the following code to create a",
    "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

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


</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},
}
```


