Datasets:
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values | focus stringclasses 13
values | answer stringlengths 1 518 | valid_answers listlengths 1 4 | question stringclasses 165
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Game rules (for this dataset):
- The board is a 15×15 grid. Player 1 = black stones (value 1). Player 2 = white stones (value 2).
- A legal move is placing exactly one stone on an empty intersection (value 0).
- A player wins immediately if they can plac... | test |
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... | perception | Q501 | four_in_a_row | 0 | [
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- A legal move is placing exactly one stone on an empty intersection (value 0).
- A player wins immediately if they can plac... | test |
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"/Users/eugenganscha/Developer/hf/gomoku_vlm_ds/reinforcement_ds/train/strategy_questions/images/sim(...TRUNCATED) | "iVBORw0KGgoAAAANSUhEUgAABCQAAAQkCAIAAAAq9FheAADSGklEQVR4nOz9C3RUx50v+lfV3lK3pNb7iSQkQAjQAxAPI2NgQhI(...TRUNCATED) | perception | Q501 | four_in_a_row | 1 | [
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"/Users/eugenganscha/Developer/hf/gomoku_vlm_ds/reinforcement_ds/train/strategy_questions/images/sim(...TRUNCATED) | "iVBORw0KGgoAAAANSUhEUgAABCQAAAQkCAIAAAAq9FheAAEAAElEQVR4nOz9C3hUx5XvDVfV3lK3pNatJSQhdOMikJAwAmzLHEF(...TRUNCATED) | strategy | Q1303 | reason_next_move | "0 0 0 1 2 0 0 0 0 0 0 0 0 0 0\n0 2 0 1 2 1 0 2 2 2 1 2 0 0 0\n0 0 1 1 2 1 2 1 0 2 1 1 1 2 0\n0 0 1 (...TRUNCATED) | ["0 0 0 1 2 0 0 0 0 0 0 0 0 0 0\n0 2 0 1 2 1 0 2 2 2 1 2 0 0 0\n0 0 1 1 2 1 2 1 0 2 1 1 1 2 0\n0 0 1(...TRUNCATED) | "You are a vision-language model analyzing Gomoku game positions.\n\nGame rules (for this dataset):\(...TRUNCATED) | test |
End of preview. Expand in Data Studio
Gomoku VLM Dataset (LoRA finetuning)
This repository contains a synthetic, image-grounded instruction dataset for training and evaluating vision-language models (VLMs) on Gomoku (15×15).
The dataset is designed for LoRA finetuning of image-text-to-text vision-language models on two complementary capabilities:
Visual
Tasks where the model must read the board image and produce a structured answer about the current position.
This includes purely perceptual objectives (cell classification, counting) and also visually grounded reasoning such as run/line detection, matrix reconstruction, end-state recognition, and yes/no tactical assessments that can be decided from the current snapshot (e.g., “immediate win exists”, “opponent threatens immediate win”).Curriculum: Curriculum-learning variant for visual skills: the training data is split into four steps that progressively move from simpler to more complex board states and visually grounded objectives (e.g., from basic cell/count tasks toward more advanced structured board understanding)
Strategy / policy (action selection)
Tasks that require choosing an action (e.g., best move / win-in-1 move selection) and decision-making that approximates a bot’s policy.
Each example includes:
- a rendered board image,
- a natural-language question, and
- one or more valid ground-truth answers (string list).
Dataset structure
This dataset is organized into multiple Hugging Face configs that mirror the repository folders:
Configs
visual
Perception-focused questions (board reading, counting, localization, etc.).
Splits:train→visual/train/*.parquetvalidation→visual/eval/*.parquet
strategy
Tactical / strategic questions (e.g., win-in-1 style tasks, move selection based on bot-policy).
Splits:train→strategy/train/*.parquetvalidation→strategy/eval/*.parquet
visual_curriculum
Step-wise curriculum training data as four growing steps:curriculum/step_1.parquetcurriculum/step_2.parquetcurriculum/step_3.parquetcurriculum/step_4.parquet
test
Test Dataset:test→test/combined.parquet
Downloading the dataset locally
Make sure the hf CLI is installed
curl -LsSf https://hf.co/cli/install.sh | bash
Source bashrc
source ~/.bashrc
Download Dataset
hf download eganscha/gomoku_vlm_ds --repo-type=dataset --local-dir ./gomoku_vlm_ds
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