datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
DGUI_HYPERMEM-JEV
DGUI_HYPERMEM-JEV
The training "brain" for DGUI-HyperMem (DeckerGUI HyperMemory) — the self-hosted
memory MCP server. Every JEV reasoning decision the service makes is appended here as a
typed instruction row, so the corpus grows with real usage and can be used to fine-tune or
few-shot the JEV layer later.
Usage
from datasets import load_dataset
ds = load_dataset("ctaxnagomi/DGUI_HYPERMEM-JEV", split="train")
for row in ds.stream():
print(row["use_case"]… See the full description on the dataset page: https://huggingface.co/datasets/ctaxnagomi/DGUI_HYPERMEM-JEV.INSTRUCT_JEV
INSTRUCT_JEV
INSTRUCT_JEV is an instruction corpus built from the TypeSafe AI documentation
for Jev, the first System One model. It is structured around the three TypeSafe
question primitives - Choice, Noul and Score - and mirrors the raw corpus
captured in deckerGUI-jev_corpus_RAW.
Credits
INSTRUCT_JEV is a DeckerGUI project and exists because of the work below.
Who
Contribution
Link
TypeSafe AI
Jev - the first System One model - and the Choice / Noul… See the full description on the dataset page: https://huggingface.co/datasets/ctaxnagomi/INSTRUCT_JEV.poker-sft-mix-v1
HoldemMix-606k
HoldemMix-606k is a 606,630-record supervised-fine-tuning corpus for
heads-up no-limit Texas hold'em (HUNL 200BB). Each record is a chat-template
exchange in which an expert assistant decides the next action at a fully
specified poker decision node, optionally accompanied by an explicit
chain-of-thought rationale.
The corpus is the training data for
jevonmao/llama31-8b-poker-mix-v1-step10k,
known as PokerLlama-4, the 8B fine-tune that accompanies the
PokerLlama-4… See the full description on the dataset page: https://huggingface.co/datasets/jevonmao/poker-sft-mix-v1.gtow-llama-sft-v3
GTO Wizard — Heads-Up NL Hold'em 200BB — SFT dataset (v3)
Supervised fine-tuning data for heads-up No-Limit Texas Hold'em, 200 big
blinds deep. Each row is a single decision point: a natural-language
description of the game state, paired with the game-theory-optimal action
GTO Wizard chose in that spot.
Intended for instruction-tuning a chat LLM to play HU 200BB poker (see the
pokerbench agent it was built for).
Schema
Two flat columns:
Column
Description… See the full description on the dataset page: https://huggingface.co/datasets/jevonmao/gtow-llama-sft-v3.postflop-solver-reasoning-traces-1m
Postflop-Solver Reasoning Traces (1M, v2)
Teacher-forced chain-of-thought reasoning traces for Heads-Up No-Limit Texas
Hold'em postflop decisions, distilled from a GTO solver (postflop-solver)
plus a strong LLM teacher.
Each example pairs a game scenario with the known-optimal solver action and
a step-by-step natural-language justification of why that action is correct.
The teacher is conditioned on the gold action (teacher forcing), so every trace
supports the correct move —… See the full description on the dataset page: https://huggingface.co/datasets/jevonmao/postflop-solver-reasoning-traces-1m.