eganscha/gomoku_vlm_ds

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: 1) a rendered board image, 2) a natural-language question, and 3) 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/*.parquet
• —validation → visual/eval/*.parquet

• —`strategy` Tactical / strategic questions (e.g., win-in-1 style tasks, move selection based on bot-policy). Splits:
• —train → strategy/train/*.parquet
• —validation → strategy/eval/*.parquet

• —`visual_curriculum` Step-wise curriculum training data as four growing steps:
• —curriculum/step_1.parquet
• —curriculum/step_2.parquet
• —curriculum/step_3.parquet
• —curriculum/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