datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
pot-o-quantum-88
pot-o-quantum-88
888,888 synthetic PoT-O challenges for tensor + MML path optimization in a Planck–tensor–realm lineage used to probe REALMS / realms-devkit themes: Planck-scale framing (Part I), tensor-network / entropy toy scales (Part IV §3–§4), and coarse-resolution “realm” tensor shapes (including 8×8 and 88×8-style blocks).
Program scale (8.88M lineage): each row carries nominal_lineage_M: 8.88 (documentation of the Quantum-88 / 8.88M Tribewarez program scale). This dataset… See the full description on the dataset page: https://huggingface.co/datasets/Tribewarez/pot-o-quantum-88.synthetic-pot-o-challenges-v1
synthetic-pot-o-challenges-v1
Tiny synthetic starter dataset for training PoT-O (Proof of Tensor Optimizations) pathfinder models.
Format (JSONL)
{"challenge": "tensor:shape=[32,64];dtype=float16;target_mml=0.42;ops:matmul,lowrank,gelu,quant4,prune0.3,transpose", "optimal_path": "path: matmul[lowrank:16] -> relu -> quant:4bit -> prune:0.35 -> score:0.418"}
challenge: Text encoding of tensor properties & allowed operations
optimal_path: Heuristic "good" optimization… See the full description on the dataset page: https://huggingface.co/datasets/Tribewarez/synthetic-pot-o-challenges-v1.synthetic-pot-o-challenges-ch7-v1
synthetic-pot-o-challenges-ch7-v1
Synthetic PoT-O challenges that prove tensor dimensioning and networking effects from the manuscript Part IV — Information-Theoretic Foundation of Spacetime (§3, §4, §7).
Format (JSONL)
Same schema as synthetic-pot-o-challenges-v1: challenge, optimal_path, difficulty, source.
Ch7 challenge strings extend the classic format with optional:
nodes=N;edges=a-b,c-d; — graph G=(V,E) for networking effects
bond_dims=d or bond_dims=d1,d2,... —… See the full description on the dataset page: https://huggingface.co/datasets/Tribewarez/synthetic-pot-o-challenges-ch7-v1.synthetic-pot-o-challanges-22-22k
synthetic-pot-o-challanges-22-22k
Large synthetic PoT-O (Proof of Tensor Optimizations) challenge set for tensor shape / dtype specs and MML (Minimum Message Length) path optimization. 22,222 examples aligned with generation signature 22.2222 (param_signature field) and MML targets sampled around 0.222222.
Format (JSONL)
Same schema as classic PoT-O challenge JSONL, plus optional lineage field:
challenge — Classic challenge string: tensor:shape=[M… See the full description on the dataset page: https://huggingface.co/datasets/Tribewarez/synthetic-pot-o-challanges-22-22k.