datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
TxT360-v2
TxT360-v2
Dataset Description
Pre-training sources for the K2 Horizon training data release. This repository is part of the K2 Horizon collection.
The repository is organized into multiple subsets. Every subset has a train split backed by Parquet shards.
K2 Horizon Dataset Series
Dataset repository
Focus
Subsets
IFM/TxT360-v2
Web and question-answering text
3
IFM/Code-Reasoning
Code reasoning and task synthesis
7
IFM/Math-Reasoning… See the full description on the dataset page: https://huggingface.co/datasets/IFM/TxT360-v2.guru-RL-92k
Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective
Dataset Description
Guru is a curated six-domain dataset for training large language models (LLM) for complex reasoning with reinforcement learning (RL). The dataset contains 91.9K high-quality samples spanning six diverse reasoning-intensive domains, processed through a comprehensive five-stage curation pipeline to ensure both domain diversity and reward verifiability.… See the full description on the dataset page: https://huggingface.co/datasets/IFM/guru-RL-92k.guru-RL-92k-extra-info-compressed
Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective
Note for this extra-info-compressed data version!
The dataset provided in this repository is specifically intended for use with the latest release of VeRL (v0.4.0). Since VeRL rl_dataset.py processes datasets as datasets.Dataset, it is essential that the structure of all Parquet files remains fully consistent. This repository is designed to meet that requirement.
In this repo, the… See the full description on the dataset page: https://huggingface.co/datasets/IFM/guru-RL-92k-extra-info-compressed.guru_previewThis is a preview version containing the same data as the official release, but without the accompanying data card. For the released version with the data card, please refer to guru-RL-92k! The data processin,g training, and evaluation code is fully open under Reasoning360~
IF_multi_constraints_upto5_SFT
Description
This dataset was derived from the allenai/IF_multi_constraints_upto5 constrained instruction-following dataset, adapting it for supervised fine-tuning. Responses were generated with google/gemma-4-31B-it and then filtered to retain only high-quality examples. The final dataset includes only samples with a minimum loose score of 1.0 and a minimum strict score of 0.8 according to the official IFBench evaluator. Maximum response length was limited to 1024 tokens, all… See the full description on the dataset page: https://huggingface.co/datasets/UniLu/IF_multi_constraints_upto5_SFT.IF-multi-constraints-upto5-LFM2.5-prompts
IF_multi_constraints_upto5 → LFM2.5 prompt format (for RLVR / rejection sampling / DPO)
A derivative of allenai/IF_multi_constraints_upto5 (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|>
Prompt-only rows (prompt_only = true): Tulu-SFT instructions with up to 5 verifiable constraints from IFEval… See the full description on the dataset page: https://huggingface.co/datasets/hungbenjamin402/IF-multi-constraints-upto5-LFM2.5-prompts.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.IF_multi_constraints_upto5_filtered_dpo_0625_filterIF_multi_constraints_upto5_filtered_sft_0625_filterIF_multi_constraints_upto5_filtered_dpo_0625_filter-keyword-filtered