datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.constraint-carrier-traces
constraint-carrier — traces & activations
Residual-stream captures, carrier-strength measurements, generated artifacts, and deterministic grades for the omission/commission constraint-decay study.
Layout (one subdirectory per model)
Qwen__Qwen3-0.6B/ traces.jsonl + activations.npz (Apple MPS, float32 pilot)
Qwen__Qwen3-1.7B/ traces.jsonl + activations.npz (RTX 5080, bf16 full pilot)
Qwen__Qwen3-4B/ traces.jsonl + activations.npz (RTX 5080, bf16 full… See the full description on the dataset page: https://huggingface.co/datasets/drlee1/constraint-carrier-traces.