Lichess/standard-chess-games
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How to use ramonpzg/gemma-4-e2b-chessking with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-E2B-it-qat-q4_0-unquantized")
model = PeftModel.from_pretrained(base_model, "ramonpzg/gemma-4-e2b-chessking")PEFT adapter trained on a bounded, game-level-split sample of short Lichess checkmates where the winner captured at most three opposing pieces. The sample contains 64 Luna-enriched games with a concrete real-world mapping and a video-scene description.
This repository contains an adapter, not a merged model or GGUF. Load it with the base model named above. The training data uses the checkpoint's own Gemma 4 chat template.
Held-out legal move rate: 1.000 (8 samples).
The exact source revision, selection rules, dataset hash, training settings,
hardware, package versions, and raw held-out replies are in run_manifest.json.
Base model
google/gemma-4-E2B