Sweaterdog/Andy-4-micro-0427-LoRA

This is the LoRA adapter for Andy-4-micro, any use of this requires full adherence to the Andy license

🤏 Andy-4-micro ⛏️

Andy-4-micro is a compact and efficient model designed specifically for Minecraft gameplay via the Mindcraft framework. Built on the Qwen2.5 1.5B architecture, it delivers surprising capabilities given its size, offering strong reasoning and reliable task execution despite being only 21% the size of the full Andy-4 model.

Andy-4-micro is licensed under the [Andy License 1.0], which requires derivatives to include the license terms and attribute the original author.

🧠 Overview

Andy-4-micro was originally an experiment—a testbed to see how much performance could be retained when compressing the Andy-4 training process into a significantly smaller base model. The result is a fast, efficient model with an excellent ability to play Minecraft and respond with logic and clarity, even under limited hardware constraints.

Despite its size, Andy-4-micro is able to:

• Understand and navigate Minecraft environments.
• Reason before responding, rather than simply reacting.
• Play autonomously or in collaboration with user input.
• Maintain high efficiency even on low-VRAM GPUs.

📊 Training Details

Andy-4-micro was trained on the same data as Andy-4-base, but with smaller architecture and unique training hyperparameters to optimize for compact performance.

• Base Model: Qwen2.5 1.5B Instruct
• Total Training Epochs: 4
  • Andy-4-base-2 dataset: 2 epochs at 4e-5 learning rate
  • FT dataset: 2 epochs at 8e-6 learning rate
• Optimizer: AdamW with cosine decay schedule
• Quantization: 4-bit (bnb-4bit)

This configuration allows Andy-4-micro to punch well above its weight class, especially when compared with other small models in the Minecraft AI space.

🔍 Differences from Andy-4-base

Feature	Andy-4-base	Andy-4-micro
Model Size	8B	1.5B
Base Architecture	DeepSeek-R1 Distill Llama 8B	Qwen2.5 1.5B
Training Duration	~3 weeks	~3 days
Usage Type	General Mindcraft agent base	Fast, efficient model for light gameplay
Reasoning	High	Medium-high
Hardware Requirements	8+	Very low (4-6GB VRAM works!)

Andy-4-micro is not a replacement for Andy-4-base or the full Andy-4 model, but instead a supplementary option for users who prioritize speed and accessibility over scale.

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🚀 Installation

You can install and run Andy-4-micro through Hugging Face and Ollama, following the same method as Andy-4-base. Here's how:

🧰 Hugging Face Quick Start

1. Go to the model page on Hugging Face.
2. Click on the "Use this model" dropdown.
3. Choose Ollama and select your quantization level:

Quantization	Minimum VRAM Required
F16	8+ GB
Q5_K_M	6+ GB
Q4_K_M	4-6 GB
Q3_K_M	4 GB (low settings)
Q2_K	2-4 GB (ultra-light)

4. Follow the Mindcraft Discord Guide for cloud-based options if needed.

🛠️ Manual Installation via Modelfile

1. Download your chosen .GGUF quantization file and the Modelfile.
2. Open Modelfile and replace the FROM path with the exact path to your .GGUF file, e.g.:

FROM C:\Users\you\Downloads\Andy-4-micro.Q3_K_M.gguf

3. In a terminal, navigate to the Modelfile directory and run:

ollama create andy-4-micro -f Modelfile

This will register the model as andy-4-micro for use with Ollama.

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🧪 Experimental Nature

Note: Andy-4-micro is an experimental release.

Although initial performance was overwhelmingly positive, Andy-4-micro was not originally designed for public deployment. However, due to strong community feedback and impressive early benchmarks, it has been released for broader use and further testing.

Expect updates, bugfixes, and potentially further fine-tuned versions based on user reports.

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⚖️ License

This model is licensed under the Andy License 1.0.
Usage must include credit to the original creator, and derivatives must remain open and under the same license.

• Author: Sweaterdog
• Acknowledgments: This model utilizes datasets and techniques developed in the Andy-4 ecosystem.
• License type: Derived from Apache 2.0 with added attribution & openness clauses.

📌 Final Notes

• Andy-4-micro is ideal for lightweight deployments, educational use, and embedded inference.
• It’s a great fit for users who want a responsive, low-resource model that still understands Minecraft deeply.
• For best results, use within the Mindcraft framework or alongside other Andy models.

We hope you enjoy exploring and building with Andy-4-micro!

"Big things come in small packages." 💡

## License
This model is licensed under the Andy 1.0 License.  
Credit: https://huggingface.co/Sweaterdog  
Acknowledgment: This work uses data and models created by @Sweaterdog.