🚀 Welcome to Next Generation Minecraft with Andy 3.6 🚀

Andy 3.6 is a LOCAL model beating Andy-3.5 in performance

Andy 3.6 is designed to be used with MindCraft, and is not designed nor intended to be used for any other applications

Please note!

Andy-3.6 was trained on older data, and not the newest and latest versions of Mindcraft.

I cannot guarantee that Andy-3.6 will work on future versions as the model was tuned to play MindCraft with a specific version!

For the rest of the Andy-3.6 generation, this model will ONLY be supported on the version of Mindcraft in this github repo!

For more info, as well as the supported version of Mindcraft, please follow this link to github

How to Install / Setup

Select the model you would like to use (The regular model, as well as the small model is recommended)
Download the Modelfile
Once downloaded, open Modelfile in a text editor, and change the path to the download location of the gguf file
When changed, save the file, and open command terminal
(Optional if CMD isn't opened via file explorer) Navigate to the correct directory using "cd"
Run the command ollama create sweaterdog/Andy-3.6 -f Modelfile If you want multiple models, include a tag afterwards. Example: sweaterdog/Andy-3.5:mini-fp16 or sweaterdog/Andy-3.6:q2_k
Go to a profile in MindCraft
Change the model to be sweaterdog/Andy-3.6 Or whatever you named your model
Ensure you have the emdedding tag set to Ollama, like below

{
    "name": "andy-3.6",

    "model": "Sweaterdog/Andy-3.6",

    "embedding": "ollama"

}

Enjoy playing with an AI that you are hosting!

How was model trained?

The model was trained on the MindCraft dataset for Andy-3.6, a curated dataset for Q & A, reasoning, and playing, which includes ~22,000 prompts.

What are capabilities and Limitations?

Andy-3.6 was trained on EVERYTHING regarding Minecraft and MindCraft, it knows how to use commands natively without a system prompt. Andy-3.6 also knows how to build / use !newAction to perform commands, it was trained on lots of building, as well as, using !newAction to do tasks like manually making something or strip mining.

What models can I choose?

There are going to be 3 model sizes avaliable, Regular, Large, and Small

Large will be a 32B parameter model, tuned from Deepseek-R1 Distilled
Regular is a 7B parameter model, tuned from Deepseek-R1 Distilled
Small is a 3B parameter model, tuned from Qwen2.5 3B

Both models will have case-by-case reasoning baked into the model, meaning when it encounters a hard task, it will reason.

You can also prompt Andy-3.6 to reason for better performance

Safety and FAQ

Q: Is this model safe to use?

A. Yes, this model is non-volatile, and cannot generate malicous content

Q. Can this model be used on a server?

A. Yes, In theory and practice the model is only capable of building and performing manual tasks via newAction

Q. Who is responsible if this model does generate malicous content?

A. You are responsible, even though the model was never trained to be able to make malicous content, there is a very very slight chance it still generates malicous code.

Q. If I make media based on this model, like photos / videos, do I have to mention the Creator?

A. No, if you are making a post about MindCraft, and using this model, you only have to mention the creator if you mention the model being used.

🔥UPDATE🔥

Andy-3.6 Release!

Andy-3.6 is our Next Generation model, feature more capabilties, and stronger performance over ANY other local LLM in Mindcraft!

I want to thank all supporters!

I would love to thank everyone who supported this project, there is a list of supporters in the files section.

You can find all of the supporters here

Performance Metrics

These benchmarks are a-typical, since most standard benchmarks don't apply to Minecraft

The benchmarks below include models via API that are cheap, and other fine-tuned local models

Zero info Prompting

How fast can a model collect 16 oak logs, and convert them all into sticks

As shown, the only models that are capable of play without information, is Andy-3.6, and all Andy-3.5 models

You can test this demo out for yourself using this profile

Time to get a stone pickaxe

For Andy-3.6, I used the Q4_K_M quantization

For Andy-3.5-mini, I used the FP16 model, I had enough VRAM to do so

For Andy-3.5, I used the Q4_K_M quantization

For Andy-3.5-small, I used the Q8_0 quantization

Andy-3.5-reasoning-small was able to be the most efficient model producing the lowest amount of messages, but took a whopping 34.5 minutes to get a stone pickaxe.

For Andy-3.5-Teensy, I used the FP16 quantization

For Mineslayerv1 and Mineslayerv2, I used the default (and only) quantization, Q4_K_M

Notes about the benchmarks

Zero Info Prompting

Andy-3.5-Mini collected 32 oak_log instead of 16 oak_log

Andy-3.5-small No notes

Andy-3.5 attempted to continue playing, and make a wooden_pickaxe after the goal was done.

Both Mineslayerv1 and Mineslayerv2 hallucinated commands, like !chop or !grab

Time to get a stone pickaxe

Andy-3.6 performed the best, beating gpt-4o-mini and claude-3.5-haiku

Andy-3.5-Mini was unable to make itself a stone pickaxe, however it collected enough wood, but then got stuck on converting logs to planks, it kept trying "!craftRecipe("wooden_planks", 6) instead of oak_planks

Andy-3.5-small kept trying to make a stone_pickaxe first

Andy-3.5 Made a stone pickaxe the faster than GPT-4o-mini and Claude-3.5-Haiku

Mineslayerv1 Was unable to use !collectBlocks, instead kept trying !collectBlock

Mineslayerv2 Was unable to play, it kept hallucinating on the first command

Sweaterdog
/

Andy-3.6