Instructions to use Mindcraft-CE/Andy-4.2-Micro-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mindcraft-CE/Andy-4.2-Micro-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Mindcraft-CE/Andy-4.2-Micro-GGUF",
	filename="andy-4.2-micro.bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Mindcraft-CE/Andy-4.2-Micro-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Mindcraft-CE/Andy-4.2-Micro-GGUF with Ollama:
```
ollama run hf.co/Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Mindcraft-CE/Andy-4.2-Micro-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Mindcraft-CE/Andy-4.2-Micro-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Mindcraft-CE/Andy-4.2-Micro-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mindcraft-CE/Andy-4.2-Micro-GGUF to start chatting

Pi new

How to use Mindcraft-CE/Andy-4.2-Micro-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Mindcraft-CE/Andy-4.2-Micro-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Mindcraft-CE/Andy-4.2-Micro-GGUF with Docker Model Runner:
```
docker model run hf.co/Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M
```

Lemonade

How to use Mindcraft-CE/Andy-4.2-Micro-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mindcraft-CE/Andy-4.2-Micro-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Andy-4.2-Micro-GGUF-Q4_K_M

List all available models

lemonade list

The Mindcraft CE team introduces Andy-4.2 Micro, noted as the best local AI you can use to play Minecraft with. Thinking faster than Andy-4.1, being able to carry out more actions than Andy-4, and rivaling models 10x it's size.

Key Innovations

Andy-4.2 Micro uses largely the same formula as Andy-4.1, but introduces a new architecture from the Qwen3.5 series which makes the model not only smarter, but more efficient. Using Gated Deltanet attention allows Andy-4.2 to run on a single RTX 3090, with 256k tokens of context, at a staggering 8-bit quantization.

Unlike the other two Andy-4.2 models, Andy-4.2 Micro has not been tested to see it's abilities to get a full set of diamond armour autonomously.

Like Andy-4.1, Andy-4.2 has vision capabilities, and has a stronger multimodal base that allows for even deeper comprehension of the game state.

How to Run

Andy-4.2 Micro is still recommended to be ran using LM Studio, we have tried using Ollama, and there were a plethora of issues, including looping, mismatched chat templates, etc;

Below are the recommended sampling parameters for Andy-4.2 Micro, but the default settings in LM Studio work great, and the model is still able to get full diamond armour by itself.

Parameter	Value
Temperature	0.6
Top K	20
Repeat Penalty	1.0
Top P	0.95
Min P	0

Model Specifications

Size: 800M Parameters
Architecture: Qwen3.5
Context Length: Up to 256 thousand tokens
Message Count: 65 messages stable
CoT Style: DeepSeek-R1 style

Training Specifications

Hardware: 1x RTX 3090
Training Time: 30 Minutes
Dataset Size: 2,748 examples
Learning Rate: 2e-5
LR Scheduler: cosine
Epoch Count: 1 Epoch
Training Quantization: 16-bit LoRA with 8-bit QaT

Testing

No testing was done on Andy-4.2 Micro

Limitations

Even though Andy-4.2 Micro is capable of incredible feats, there is one domain where it does not perform well: Building. During internal testing any time Andy-4.2 would use !newAction, it would produce thousands and thousands of tokens, but never do anything. It is not advised to use Andy-4.2 as your code model.

Apart from that. Andy-4.2 Micro has shown to be our most hard-working model yet, and navigates potential errors very well.

What's Next?

Based on the lessons from Andy-4.2 models, the Mindcraft team is prepared to collect better training data, explore new architectures to make the cost of running Andy models cheaper, as well as packing more brains into these tiny minds.

Licenses and Notices

Like all other Andy models, Andy-4.2 Micro is based on the Andy license of terms. Being generally permissive, it contains qualifiers as to what makes an "Andy" class model.

See Andy 2.0 License.

This work uses data and models created by @Sweaterdog.