🧠 Andy‑4 Family

A unified repository for the Andy‑4 family of specialist AI models, each tuned for enhanced Minecraft gameplay and multimodal capabilities* via the Mindcraft framework.

*Multimodal variants are coming, and are not yet released

Shared Metadata

• Language: English (en)
• Tags: gaming, minecraft, mindcraft
• License: Andy 1.1 License

🧠 Andy‑4 ⛏️

Andy‑4 is an 8 billion‑parameter specialist model tuned for Minecraft gameplay via the Mindcraft framework. Trained on a single NVIDIA RTX 3090 over three weeks, Andy‑4 delivers advanced reasoning, multi‑step planning, and robust in‑game decision‑making.

⚠️ Certification: Andy‑4 is not yet certified by the Mindcraft developers. Use in production at your own discretion.

🔍 Model Specifications

• Parameter Count: 8 B
• Training Hardware: 1 × NVIDIA RTX 3090
• Training Duration: ~3 weeks
• Data Volumes:
  • Messages: 179,384
  • Tokens: 425,535,198
  • Conversations: 62,149
• License: Andy 1.1 License
• Repository: Andy-4

Datasets

datasets:
  - Sweaterdog/Andy-4-base-1
  - Sweaterdog/Andy-4-base-2
  - Sweaterdog/Andy-4-ft
language:
  - en
base_model:
  - unsloth/Llama3.1-8B
tags:
  - gaming
  - minecraft
  - mindcraft

📊 Training Regimen

1. Andy‑4‑base‑1 dataset (47.4 k examples)

   • Epochs: 2
   • Learning Rate: 7 × 10⁻⁵

2. Andy‑4‑base‑2 dataset (48.9 k examples)

   • Epochs: 4
   • Learning Rate: 3 × 10⁻⁷

3. Fine‑tune (FT) dataset (4.12 k examples)

   • Epochs: 2.5
   • Learning Rate: 2 × 10⁻⁵

• Optimizer: AdamW_8bit with cosine decay
  
• Quantization: 4‑bit (bnb-4bit) for inference
  
• Warm‑Up Steps: 0.1% of each dataset

🚀 Installation

First, choose your quantization (context window base: 8192):

1. Installation via Ollama

1. Select your desired quantization
   
2. Copy the provided ollama run command
   
3. Execute it in your terminal
   
4. Use the model (e.g., ollama/sweaterdog/andy-4:latest)

2. Manual Download & Setup

1. Download:

   • From the Hugging Face Files tab, download the .GGUF weights (e.g., Andy-4.Q4_K_M.gguf).
     
   • Retrieve the Modelfile from this repo.

2. Configure Modelfile:

   FROM /path/to/Andy-4.Q4_K_M.gguf
   

   Optional: Adjust num_ctx for extended context windows if you have sufficient VRAM.

3. Register Locally:

   ollama create andy-4 -f Modelfile
   

If you lack a GPU, refer to the Mindcraft Discord guide for free cloud options.

🔧 Context‑Window Quantization

To reduce VRAM usage for context caching:

Windows

1. Close Ollama.
   
2. In System Properties → Environment Variables, add:

   
   OLLAMA_FLASH_ATTENTION=1  
   OLLAMA_KV_CACHE_TYPE=q8_0   # or q4_0 (more savings, less stable)
   

3. Restart Ollama.

Linux/macOS

export OLLAMA_FLASH_ATTENTION=1
export OLLAMA_KV_CACHE_TYPE="q8_0"   # or "q4_0"
ollama serve

📌 Acknowledgments

• Data & Models By: @Sweaterdog
  
• Framework: Mindcraft
  
• LoRA Weights: https://huggingface.co/Sweaterdog/Andy-4-LoRA

🤏 Andy‑4‑micro 🧠

Andy‑4‑micro is a lightweight, 1.5 B‑parameter variant of Andy‑4, optimized for responsive local inference and experimentation within the Mindcraft framework.

💡 Trained on a single NVIDIA RTX 3070 over four days, Andy‑4‑micro maintains strong performance while staying efficient.
⚠️ Certification: Not yet certified by Mindcraft developers. Use at your own discretion.

📊 Model Overview

• Parameter Count: 1.5 B
  
• Training Hardware: 1 × NVIDIA RTX 3070
  
• Training Duration: ~4 days
  
• Total Tokens: ~42 M
  
• Base Architecture: Qwen 2.5
• License: Andy 1.1 License
  
• Repository: Andy-4-micro

Datasets

datasets:
  - Sweaterdog/Andy-4-base-2
  - Sweaterdog/Andy-4-ft
language:
  - en
base_model:
  - unsloth/Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit
tags:
  - gaming
  - minecraft
  - mindcraft

🚀 Installation

First, choose your quantization (context window base: 8192):

1. Installation via Ollama

1. Select quantization
   
2. Copy and run the ollama run command
   
3. Use ollama/sweaterdog/andy-4:micro-q8_0 locally

2. Manual Download & Setup

1. Download: Grab the .GGUF weights (e.g., Andy-4-micro.Q4_K_M.gguf) and Modelfile.
   
2. Configure Modelfile:

   
   FROM /path/to/Andy-4-micro.Q4_K_M.gguf
   

   Optional: Tweak num_ctx as needed.
3. Register:

   
   ollama create andy-4-micro -f Modelfile
   

Free GPU options: see the Mindcraft Discord guide.

🔧 Context‑Window Quantization

Use the same environment-variable tweaks listed above for AdamW-flash attention and KV cache.

📌 Acknowledgments

• Data & Model By: @Sweaterdog
  
• Framework: Mindcraft
  
• LoRA Weights: https://huggingface.co/Sweaterdog/Andy-4-micro-LoRA