README.md

metadata

license: cc-by-4.0
language:
  - en
task_categories:
  - text-generation
tags:
  - tool-use
  - function-calling
  - agentic
  - lfm2
  - lfm2.5
  - liquid-ai
  - pythonic-tool-calls
size_categories:
  - 100K<n<1M
source_datasets:
  - nvidia/Nemotron-SFT-Agentic-v2
configs:
  - config_name: default
    data_files:
      - split: tool_calling
        path: data/tool_calling-*.parquet
      - split: interactive_agent
        path: data/interactive_agent-*.parquet
      - split: search
        path: data/search-*.parquet

Nemotron-SFT-Agentic-v2 → LFM2.5 Pythonic tool-call format

A derivative of nvidia/Nemotron-SFT-Agentic-v2 (CC-BY-4.0) normalized for supervised fine-tuning of Liquid AI LFM2 / LFM2.5 models, whose native tool-call format is Pythonic:

<|im_start|>assistant
<|tool_call_start|>[get_weather(location='Paris, France', unit='celsius')]<|tool_call_end|><|im_end|>

Every row was rendered through the official LiquidAI/LFM2.5-VL-3B chat template (identical to the LFM2.5 text models' template for text-only input) and round-trip verified: each rendered Pythonic call was parsed back and compared to the source call's name and arguments. Rows that did not round-trip exactly were dropped.

What changed vs. the source

Transform	Why
tool_calls[].function.arguments JSON string → dict	The LFM template raises on string arguments.
reasoning_content and inline <think>…</think> removed	LFM2.5 (non-Thinking) models answer directly.
Tool names sanitized to Python identifiers (web-search → web_search), consistently in tools and calls; originals kept in renamed_tools	Pythonic call syntax requires identifiers.
Empty system turns, legacy function_call, trailing non-assistant turns removed	Template hygiene.
Dropped: rows with filter_reason set, calls to undeclared tools, unparseable arguments, argument names that are Python keywords (from, class — unrepresentable as Pythonic kwargs), rows over 8192 tokens, rows with no assistant tokens	Training hygiene.

Nothing was rephrased, re-generated, or re-labelled. Teacher-model provenance is preserved per row.

Columns

Column	Type	Description
id	str	source uuid
source, split, license	str	provenance
domain, teacher_model	str	from source where present
messages	str (JSON)	canonical OpenAI-style messages; tool_calls arguments are dicts. Feed this + tools to apply_chat_template for training with any LFM template version.
tools	str (JSON)	OpenAI-style tool schemas
text	str	fully rendered LFM2.5 conversation, BOS included — ready for packing
n_turns, n_tool_calls, n_tools, n_tokens, n_assistant_tokens	int	sizes (LFM2.5 tokenizer)
renamed_tools	str (JSON)	{original: sanitized} when any tool was renamed, else ""

Stats

Split	Read	Kept	Rows with renamed tools	Dropped (reason=count)
tool_calling	707,052	603,007	27,281	bad_arg_name=1,364, call_unknown_tool=6,783, empty_assistant=5,651, keyword_arg_name=119, roundtrip_parse=12, too_long=83,909, tool_name_collision=6,207
interactive_agent	278,880	278,570	0	too_long=310
search	5,968	287	287	too_long=5,681

Training notes

• Use assistant-only loss. The LFM template has {% generation %} markers, so apply_chat_template(messages, tools=tools, tokenize=True, return_assistant_tokens_mask=True) gives the mask directly.
• Prefer re-rendering from messages/tools over training on text if you fine-tune a model whose template differs.
• Serving-side parsers must accept JSON literals (true, null, nested {}/[]) inside Pythonic calls — the template emits them via tojson for non-string values.

Citation

Please cite the upstream dataset: NVIDIA, Nemotron-SFT-Agentic-v2, 2025. https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2