license: apache-2.0
datasets:
- Sweaterdog/Andy-3.5-MASSIVE
- Sweaterdog/Andy-3.5
- Sweaterdog/Andy-3.5-reasoning
language:
- en
base_model:
- unsloth/DeepSeek-R1-Distill-Qwen-7B-unsloth-bnb-4bit
tags:
- minecraft
- Mindcraft
- Minecraft
- MindCraft
🚀 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
Installing Andy-3.6 is much easier and Andy-3.5!
- In the top right of this repo, click "Use This Model"
- Next, click Ollama
- Pick your quantization
- Run the command in your terminal
- Now you have Andy-3.6 installed!
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