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hungbenjamin402/IF-multi-constraints-upto5-SFT-LFM2.5

IF_multi_constraints_upto5_SFT → LFM2.5 chat format A derivative of UniLu/IF_multi_constraints_upto5_SFT (odc-by) normalized for fine-tuning 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|> SFT-ready precise-instruction-following pairs: the allenai IF-RLVR prompts answered by Gemma-4-31B-it and filtered with the official IFBench… See the full description on the dataset page: https://huggingface.co/datasets/hungbenjamin402/IF-multi-constraints-upto5-SFT-LFM2.5.

sourceHugging Faceodc-byupdated 1mo agoView on Hugging Face

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IFmulticonstraintsupto5SFT → LFM2.5 chat format

A derivative of UniLu/IF_multi_constraints_upto5_SFT (odc-by) normalized for fine-tuning 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|>

SFT-ready precise-instruction-following pairs: the allenai IF-RLVR prompts answered by Gemma-4-31B-it and filtered with the official IFBench verifier (loose 1.0 / strict ≥ 0.8, ≤ 1024 tokens). extra keeps strict_score, loose_score, response_length_words.

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
`{prompt, response}` → user/assistant messages	Chat format.
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.
Exact-duplicate tool entries deduplicated	Template hygiene.
Dropped: calls to undeclared tools, unparseable arguments, argument names that are not identifiers or are Python keywords (`from`, `class`), rows over 8192 tokens, rows with no assistant tokens	Training hygiene.

Nothing was rephrased, re-generated, or re-labelled.

Columns

Column	Type	Description
`id`, `source`, `split`, `license`	str	provenance
`messages`	str (JSON)	canonical OpenAI-style messages; `tool_calls` arguments are dicts. Feed this + `tools` to `apply_chat_template` to re-render with any LFM template version.
`tools`	str (JSON)	OpenAI-style tool schemas
`text`	str	fully rendered LFM2.5 conversation, BOS included
`prompt_only`	bool	true when the row is a prompt ending in the generation prompt (no assistant tokens)
`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 `""`
`extra`	str (JSON)	source-specific fields: `key`, `strict_score`, `loose_score`, `response_length_words`

Stats

Split	Read	Kept	Rows with renamed tools	Dropped (reason=count)
`train`	42,161	42,161	0

Training notes

—Use assistant-only loss: apply_chat_template(messages, tools=tools, tokenize=True, return_assistant_tokens_mask=True).
—Prefer re-rendering from messages/tools over training on text if your template differs.
—Serving-side parsers must accept JSON literals (true, null, nested {}/[]) inside Pythonic calls.

Citation

Please cite the upstream dataset: https://huggingface.co/datasets/UniLu/IFmulticonstraintsupto5SFT