Post
461
📖 A gamebook you can play in your own words, powered by local Decision Models.
Traditional gamebooks offer explicit branches: “To enter the forest, turn to page 42.” In Choose your own adventure, you can describe an action, question a character, or ask what you already know before deciding. Clickable choices remain available.
The story’s branches and consequences are authored. Natural language adds a flexible way to interact with them, with conversations between actions.
I built this project to explore a concrete role for Decision Models in an interactive application:
• Kev-4B makes structured decisions: matching intentions to available actions and distinguishing acting from discussing.
• Ink manages story progression, discoveries, inventory and endings.
• Qwen3-8B selects dialogue responses from authored content available in the current state.
Both models run locally inside the Space through llama.cpp, using quantized GGUFs. You can inspect the model calls, scores and routing decisions directly in the interface.
The current release includes three adventures in French and English, with a pack format for adding new stories without changing the engine.
It’s still experimental, and interpretations can go wrong. I’d love feedback on how it feels to play—and on this use of Decision Models to connect natural language with explicit game logic.
Try and play here: fffiloni/choose-your-own-adventure-dm 🤗
Traditional gamebooks offer explicit branches: “To enter the forest, turn to page 42.” In Choose your own adventure, you can describe an action, question a character, or ask what you already know before deciding. Clickable choices remain available.
The story’s branches and consequences are authored. Natural language adds a flexible way to interact with them, with conversations between actions.
I built this project to explore a concrete role for Decision Models in an interactive application:
• Kev-4B makes structured decisions: matching intentions to available actions and distinguishing acting from discussing.
• Ink manages story progression, discoveries, inventory and endings.
• Qwen3-8B selects dialogue responses from authored content available in the current state.
Both models run locally inside the Space through llama.cpp, using quantized GGUFs. You can inspect the model calls, scores and routing decisions directly in the interface.
The current release includes three adventures in French and English, with a pack format for adding new stories without changing the engine.
It’s still experimental, and interpretations can go wrong. I’d love feedback on how it feels to play—and on this use of Decision Models to connect natural language with explicit game logic.
Try and play here: fffiloni/choose-your-own-adventure-dm 🤗
