 | I made a program that converts any llama2 large language model into a minecraft datapack, and you can run inference right inside the game. It's still semi-finished, Currently I've only implemented argmax sampling, so the output tends to stuck in loops sometimes. Adding top-p sampling will probably improve this a lot. The tokenizer is also missing for now, it can only generate text from scratch. Inference speed is...quite slow. With a 15M parameter model, it takes roughly 20 minutes to produce a single token. If you want to try it out yourself, you can download "stories15M.bin" and "tokenizer.bin" from llama2.c, and follow the instructions in my repository down below. I will keep working on this project, hopefully one day I will be able to bring a usable chat model in Minecraft. *Inspired by Andrej Karpathy's llama2.c [link] [comments] |
Running Llama2 Models in Vanilla Minecraft With Pure Commands
Reddit r/LocalLLaMA / 4/4/2026
💬 OpinionSignals & Early TrendsTools & Practical UsageModels & Research
Key Points
- A developer created a tool that converts Llama2 language models into a Minecraft datapack so text generation (inference) can run inside the game using only vanilla commands.
- The current implementation is semi-finished, supporting only argmax sampling and lacking a tokenizer, which limits outputs and can cause the model to get stuck in repetitive loops.
- The project notes that adding top-p sampling and completing the tokenizer would likely improve generation quality and usability, including supporting more natural continuation.
- Performance is reported as extremely slow: a ~15M parameter model may take around 20 minutes to generate a single token, making it more of a proof-of-concept than a practical chat experience today.
- Users are invited to try it by downloading model and tokenizer binaries (e.g., from the llama2.c ecosystem) and following the repository instructions; the author plans to keep improving toward a usable Minecraft chat model.
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