KataGo x LLM Explainable AI

Fine-tuning LLMs to explain Go strategies using serialized game states.

Role: Project Lead Stack: Python, Qwen2.5, Hugging Face, KataGo

[cite_start]As the Project Lead for this initiative, I am coordinating a team of 6 contributors to build an Explainable AI system for the game of Go[cite: 35, 40]. The goal is to fine-tune Large Language Models (LLMs) to provide natural language explanations for complex game situations.

Key Technical Contributions:

  • Custom GTP Proxy: Wrote my_gtp_proxy.py to intercept Go Text Protocol (GTP) commands from Lizzie/KataGo. [cite_start]This allows us to serialize game states and KataGo’s analysis into JSONL format in real-time[cite: 36].
  • Data Pipeline: Designed a pipeline to parse SGF records and query KataGo-18B. [cite_start]We extract top-k candidate moves along with win-rates and score-leads to create a filtered dataset for reward signals[cite: 37].
  • LLM Fine-tuning: Adapted Hugging Face scripts to fine-tune Qwen2.5-7B-Instruct using LORA/GRPO techniques. [cite_start]I also set up local inference prototyping using LM Studio[cite: 39].