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# nemo_automodel.components.datasets.llm.mock

## Module Contents

### Classes

| Name                                                                                                  | Description                                                                               |
| ----------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| [`MockUnpackedDatasetConfig`](#nemo_automodel-components-datasets-llm-mock-MockUnpackedDatasetConfig) | Construction-time configuration for the mock unpacked dataset (tokenizer is a build arg). |

### Functions

| Name                                                                                            | Description                                                           |
| ----------------------------------------------------------------------------------------------- | --------------------------------------------------------------------- |
| [`build_unpacked_dataset`](#nemo_automodel-components-datasets-llm-mock-build_unpacked_dataset) | Build a dataset where each example is one sentence (variable length). |
| [`gen_sentence_ids`](#nemo_automodel-components-datasets-llm-mock-gen_sentence_ids)             | Sentence generator with Gaussian length control.                      |
| [`make_vocab`](#nemo_automodel-components-datasets-llm-mock-make_vocab)                         | Build a trivial vocab; index 0=\<pad>, 1=\<eos>, rest = tok\_i.       |

### Data

[`ds`](#nemo_automodel-components-datasets-llm-mock-ds)

### API

```python
class nemo_automodel.components.datasets.llm.mock.MockUnpackedDatasetConfig(
    num_sentences: int = 10,
    mean_len: float = 20.0,
    std_len: float = 6.0,
    vocab_size: int = 100,
    max_sentence_len: int = 64,
    seed: int = 0
)
```

Dataclass

Construction-time configuration for the mock unpacked dataset (tokenizer is a build arg).

Maximum sentence length.

Mean sentence length (Gaussian).

Number of sentence examples to generate.

Seed for the random generator.

Standard deviation of sentence length (Gaussian).

Vocabulary size for the synthetic tokens.

```python
nemo_automodel.components.datasets.llm.mock.MockUnpackedDatasetConfig.build(
    tokenizer: 'PreTrainedTokenizerBase | None' = None
) -> datasets.Dataset
```

Build the mock unpacked :class:`~datasets.Dataset` from this :class:`MockUnpackedDatasetConfig`.

```python
nemo_automodel.components.datasets.llm.mock.build_unpacked_dataset(
    num_sentences: int = 10,
    mean_len: float = 20.0,
    std_len: float = 6.0,
    vocab_size: int = 100,
    max_sentence_len: int = 64,
    seed: int = 0,
    tokenizer = None
)
```

Build a dataset where each example is one sentence (variable length).

**Returns:**

* a HuggingFace Dataset with fields:
  input\_ids:     Sequence(int64)
  attention\_mask:Sequence(int8)
  labels:        Sequence(int64)
  position\_ids:  Sequence(int64)

```python
nemo_automodel.components.datasets.llm.mock.gen_sentence_ids(
    vocab,
    mean_len: float,
    std_len: float,
    max_len: int
)
```

Sentence generator with Gaussian length control.

```python
nemo_automodel.components.datasets.llm.mock.make_vocab(
    vocab_size: int = 100
)
```

Build a trivial vocab; index 0=\<pad>, 1=\<eos>, rest = tok\_i.

```python
nemo_automodel.components.datasets.llm.mock.ds = build_unpacked_dataset(num_sentences=5, mean_len=12.0, std_len=3.0, vocab_size=5...
```