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.northeast-india-tribes-and-subtribes
Northeast India Tribes and Sub-Tribes Dataset 🌏
This dataset is the first structured open-source collection of tribes and sub-tribes across the 8 states of Northeast India:
Arunachal Pradesh
Assam
Manipur
Meghalaya
Mizoram
Nagaland
Tripura
Sikkim
📊 Dataset Overview
Entries: 118+
Fields:
State
Tribe
Sub-tribes / Clans
Regions / Districts
Language(s) Spoken
Notes on Distribution
Linguistic Family
🧭 Purpose
Support LLM training for Northeast Indian… See the full description on the dataset page: https://huggingface.co/datasets/MWirelabs/northeast-india-tribes-and-subtribes.finetuning_demo2finetuning_demosynthetic-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.tribe-replay-heatmaps
TRIBE Replay Heatmaps
Video-ID manifest and per-video results for the paper A global predicted-fMRI drive signal from TRIBE does not predict YouTube replay heatmaps.
Paper: https://arxiv.org/abs/2607.01400
Code: https://github.com/mercurialsolo/tribe-replay-heatmaps
Summary
We run TRIBE, the winning model of the Algonauts 2025 brain-encoding challenge combining Llama-3.2, V-JEPA2 and Wav2Vec-BERT, on 48 YouTube videos. We reduce its predicted cortical response to… See the full description on the dataset page: https://huggingface.co/datasets/mercurialsolo/tribe-replay-heatmaps.psy-q-graph-369666
psy-q-graph-369666
369,666 synthetic abstract pathway-graph records in PoT-O-style challenge / optimal_path form. Part of the 369.666.444 (Psy-Q-Finder 369M) program. Graphs use fictional node types (meta, route, guard, probe) and weighted edges — not real molecules, CAS IDs, or laboratory procedures.
Paired base model: Tribewarez/psy-q-finder-369M.
Record schema
Field
Meaning
challenge
Single-line graph spec: nodes, edges, start, goal, forbidden edge… See the full description on the dataset page: https://huggingface.co/datasets/Tribewarez/psy-q-graph-369666.psy-q-scene-369666
psy-q-scene-369666
369,666 rows of fully synthetic short prose in a Goa / psychedelic-scene-adjacent register: imaginary flyers, DJ blurbs, travelogue scraps, and neutral public-service tone. Not scraped from forums. Not traditional or Indigenous knowledge. Not depicting real places or ceremonies.
Paired base model: Tribewarez/psy-q-finder-369M.
Record schema
Field
Meaning
text
1–3 short paragraphs (fits ~965-token windows when tokenized loosely)
register… See the full description on the dataset page: https://huggingface.co/datasets/Tribewarez/psy-q-scene-369666.LeatherTrainLeatherMathSeries3synthetic-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.MathSeriesfinetuning_demo3finetuning_demo4my-distiset-1e19c746
Dataset Card for my-distiset-1e19c746
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/Tribemind/my-distiset-1e19c746/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/Tribemind/my-distiset-1e19c746.finetuning_demo5blackHoles