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GameDecider: a 400M model that answers your game's "what happens next?" questions, open weights (Apache-2.0
GameDecider is an encoder (Ettin-400M base) fine-tuned to take *a scene + a question + options* and return a
probability for each option, or say "not sure". It runs locally on CPU or GPU (ONNX: about 0.1 s per check on an RTX 3060 Ti, about 1 s on a 6-core desktop CPU; up to 5 questions about the same scene
cost the same as one), and it ships with compiled adapters for common NPC questions.
What's in the repo:
- ONNX weights (fp16/fp32) + tokenizer, calibrated thresholds:
(abstains more; for setups that hand "not sure" to an LLM)
- 39 compiled adapters for common NPC questions (threat, apology, persuasion, quest accept, ...), plus SkyrimNet-style memory matching and NPC mood, and a character-reaction adapter (14 reaction animations)
- Model card with evals, data sources and licences, a minor-safety section, and known limits; 200 sample rows
Honest numbers (private held-out sets, CIs in the card): 0.600 on task families it never saw (heldout_v3), 0.707 on real
game transcripts. Planning: 0.939 on our procedural "what should I do next to reach X?" eval, but the training data has
rows from the same generator, so treat that as in-family, not general planning; use an LLM or a planner for real plans.
Training data: ~334k decision rows from permissive datasets (MIT / Apache / CC-BY / ODC-By, all credited) plus
procedurally generated game scenarios written by open models. Removed before release: anything without a clear licence
and anything generated by models whose terms forbid training.
Try it: P1ayer-1/gamedecider-playground Β· Weights: holdoutlabs/gamedecider
Feedback very welcome β especially question types it gets wrong in your game.
GameDecider is an encoder (Ettin-400M base) fine-tuned to take *a scene + a question + options* and return a
probability for each option, or say "not sure". It runs locally on CPU or GPU (ONNX: about 0.1 s per check on an RTX 3060 Ti, about 1 s on a 6-core desktop CPU; up to 5 questions about the same scene
cost the same as one), and it ships with compiled adapters for common NPC questions.
What's in the repo:
- ONNX weights (fp16/fp32) + tokenizer, calibrated thresholds:
default (answers ~97% of the time) and strict(abstains more; for setups that hand "not sure" to an LLM)
- 39 compiled adapters for common NPC questions (threat, apology, persuasion, quest accept, ...), plus SkyrimNet-style memory matching and NPC mood, and a character-reaction adapter (14 reaction animations)
- Model card with evals, data sources and licences, a minor-safety section, and known limits; 200 sample rows
Honest numbers (private held-out sets, CIs in the card): 0.600 on task families it never saw (heldout_v3), 0.707 on real
game transcripts. Planning: 0.939 on our procedural "what should I do next to reach X?" eval, but the training data has
rows from the same generator, so treat that as in-family, not general planning; use an LLM or a planner for real plans.
Training data: ~334k decision rows from permissive datasets (MIT / Apache / CC-BY / ODC-By, all credited) plus
procedurally generated game scenarios written by open models. Removed before release: anything without a clear licence
and anything generated by models whose terms forbid training.
Try it: P1ayer-1/gamedecider-playground Β· Weights: holdoutlabs/gamedecider
Feedback very welcome β especially question types it gets wrong in your game.
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