datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
lemmanaid-afp-reruns
Lemmanaid AFP-pool Reproducibility Reruns
Reproducibility study for claude-opus-4-5 on the yalhessi/lemexp-commerical-llm-experiment benchmark, using an AFP demo pool (honest eval — no train/test theory leakage).
Companion to ggranberry/lemmanaid-commercial-results, which holds the earlier shot-count + retrieval sweeps under test-LOO.
Configs
Two configs, one per benchmark domain:
Config
Source HF config
Test rows
octonions
template_octonions_2026… See the full description on the dataset page: https://huggingface.co/datasets/ggranberry/lemmanaid-afp-reruns.nepali-fruit-rerun
Nepali Source-Grounded Instruction Dataset
Synthetic Nepali instruction-tuning data generated with NVIDIA NeMo Data
Designer from authoritative Nepali documents (agriculture manuals, legal
texts). Answers are grounded strictly in the source; unanswerable questions
get an explicit refusal. Records use chat messages format plus metadata
and per-record quality_scores (grounding / correctness / naturalness, 1-5,
LLM-as-judge). One data/train-<shard>.jsonl per source document; shards… See the full description on the dataset page: https://huggingface.co/datasets/aarajbhattarai/nepali-fruit-rerun.rejected-nepali-fruit-rerun
Nepali Source-Grounded Instruction Dataset — REJECTED
Synthetic Nepali instruction-tuning data generated with NVIDIA NeMo Data
Designer from authoritative Nepali documents (agriculture manuals, legal
texts). Answers are grounded strictly in the source; unanswerable questions
get an explicit refusal. Records use chat messages format plus metadata
and per-record quality_scores (grounding / correctness / naturalness, 1-5,
LLM-as-judge). One data/train-<shard>.jsonl per source… See the full description on the dataset page: https://huggingface.co/datasets/aarajbhattarai/rejected-nepali-fruit-rerun.humaneval-rerun-scoresfixed-n-rb-cost-aware-marginrl-qwen3-1.7b-base-math12k-token-mean-rerun-rollouts
fixed_n_rb_cost_aware_marginrl_Qwen3-1.7B-Base_math12k_token_mean_rerun rollouts
This dataset contains one compressed JSONL shard for every completed training
step. The step and rollout_index columns uniquely locate a rollout within
this training run. Run metadata and per-step row counts are recorded in
rollout_manifest.json.
7b_iter2_rlcf_worst_rerun_0.17_rlcf_worst_expand_tokenized_gap_ratio_0.177b_iter2_rlcf_worst_rerun_0.17_rlcf_worst_expand_tokenized_gap_ratio_0.17_logprob2.rerun_hintarc-agi-mixed-max4096-impabs-dpo-lr1e-7-beta0.1-16samp-rerun-flat-curr-valid7b_iter2_rlcf_worst_rerun_0.17_preprocessedlevir-yolov8n-p2-gap-factorized-tal-rerun-seed4206-03-rerun
Dreame 06-03 SpatialLM voxel comparison
This dataset contains Rerun recordings and SpatialLM text outputs for the same
colored Open3D TSDF point cloud reconstructed from the Dreame 06-03 active
stereo sequence. It compares SpatialLM point-cloud cleanup voxel sizes of
0.075 m and 0.025 m.
Files
Path
Description
active_stereo_tsdf_point_cloud.rrd
Existing historical 0.075 m Rerun recording.
runs/voxel_0.075/
Historical layout, raw generation, and… See the full description on the dataset page: https://huggingface.co/datasets/HanningLiu/06-03-rerun.tinyperson-yolov9t-cp3-mosaic-no-oacp-rerunHuggingFaceTB_SmolLM2-MagpieUltraPlus-OHhendrycks-math-mc-llama-sft-regen-rerun-part1-of-4game_stage2_rerun_zjhhhh__qwen2.5_3B_Instruct_min_stage2_seed_555134_eta_1e4_step_301gradmem-award-rerun-20260730HuggingFaceTB_SmolLM2-MagpieUltraPlusarc-agi-mixed-max4096-impabs-dpo-lr1e-7-beta0.01-16samp-rerun-flat-abs-9of32mmlu_rerunQwen7b_rlcf_worst_rerun_0.17_iter1_0Qwen7b_rlcf_worst_rerun_0.17_iter1_80007b_iter2_rlcf_worst_rerun_0.17_vec_rlcf_scores_177b_iter2_rlcf_worst_rerun_0.17_vec_rlcf_scores_42Qwen_Qwen2.5-1.5B-Instructgeneration_for_iter2_7b_rlcf_worst_rerun_0.17_part4arc-agi-mixed-max4096-impabs-dpo-lr1e-7-beta0.1-16samp-rerun-flat-abs-2of32Qwen7b_rlcf_worst_rerun_0.17_iter1_10000Qwen7b_rlcf_worst_rerun_0.17_iter1_20000Qwen7b_rlcf_worst_rerun_0.17_iter1_24000
