nemo_automodel.components.datasets.llm.xlam#

Module Contents#

Functions#

_json_load_if_str

_convert_tools

Convert xLAM tool definitions into OpenAI tool schema.

_convert_tool_calls

Convert xLAM answers field into OpenAI tool_calls messages.

_format_example

make_xlam_dataset

Load and preprocess the xLAM function-calling dataset to OpenAI messages compatible with the bulbasaur chat template (tool-calling aware).

Data#

logger

_TYPE_MAP

API#

nemo_automodel.components.datasets.llm.xlam.logger#

‘getLogger(…)’

nemo_automodel.components.datasets.llm.xlam._TYPE_MAP#

None

nemo_automodel.components.datasets.llm.xlam._json_load_if_str(value)#
nemo_automodel.components.datasets.llm.xlam._convert_tools(
raw_tools: List[Dict],
) → List[Dict]#

Convert xLAM tool definitions into OpenAI tool schema.

nemo_automodel.components.datasets.llm.xlam._convert_tool_calls(
raw_calls: List[Dict],
example_id: Optional[int] = None,
) → List[Dict]#

Convert xLAM answers field into OpenAI tool_calls messages.

nemo_automodel.components.datasets.llm.xlam._format_example(
example,
tokenizer,
eos_token_id,
pad_token_id,
seq_length=None,
padding=None,
truncation=None,
)#
nemo_automodel.components.datasets.llm.xlam.make_xlam_dataset(
tokenizer,
seq_length=None,
limit_dataset_samples=None,
fp8=False,
split='train',
dataset_name='Salesforce/xlam-function-calling-60k',
padding=False,
truncation=False,
)#

Load and preprocess the xLAM function-calling dataset to OpenAI messages compatible with the bulbasaur chat template (tool-calling aware).

Each example is formatted as:

  • user: the natural language query

  • assistant: emits tool_calls with serialized arguments

  • tools: OpenAI function schema derived from the dataset tool specs