Instructions to use imperialdiva/terraria-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use imperialdiva/terraria-assistant with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir terraria-assistant imperialdiva/terraria-assistant
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Terraria Assistant (RAG + Phi-3-mini)
A question-answering assistant for the game Terraria. It answers questions about crafting, items, classes, and bosses, grounding every answer in the Terraria Wiki using Retrieval-Augmented Generation (RAG).
How it works
- Your question is matched against chunks of the Terraria Wiki using semantic search (sentence-transformers + FAISS).
- The most relevant wiki text is passed to Phi-3-mini, which answers based only on that context. This reduces hallucination.
The repo also includes optional LoRA adapters fine-tuned on Terraria Q&A pairs (in adapters/), for use with Apple's MLX on Apple Silicon.
Requirements
- Python 3.11
- Apple Silicon Mac (for MLX)
Setup
pip install -r requirements.txt
Usage
python rag_chat.py "How do I craft a Molten Pickaxe?"
Example output: You need 20 Hellstone Bars, crafted at an Iron Anvil or Lead Anvil.
Strengths and limits
- Strong: crafting recipes, item stats, boss mechanics.
- Weaker: specific gear recommendations may occasionally be inaccurate.
- Only answers Terraria questions; off-topic questions are declined.
- Not an official source; verify important details on the wiki.
Data source
Data collected from Terraria Wiki (https://terraria.wiki.gg), licensed under CC BY-NC-SA. Non-commercial use only. Fan-made, not affiliated with Re-Logic.
Hardware compatibility
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Model tree for imperialdiva/terraria-assistant
Base model
microsoft/Phi-3-mini-4k-instruct