Instructions to use Sweaterdog/Andy-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sweaterdog/Andy-4 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Sweaterdog/Andy-4",
	filename="Andy-4.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Sweaterdog/Andy-4 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Sweaterdog/Andy-4:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Sweaterdog/Andy-4:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Sweaterdog/Andy-4:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Sweaterdog/Andy-4: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 Sweaterdog/Andy-4:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Sweaterdog/Andy-4: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 Sweaterdog/Andy-4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sweaterdog/Andy-4:Q4_K_M

Use Docker

docker model run hf.co/Sweaterdog/Andy-4:Q4_K_M

LM Studio
Jan
Ollama
How to use Sweaterdog/Andy-4 with Ollama:
```
ollama run hf.co/Sweaterdog/Andy-4:Q4_K_M
```

Unsloth Studio

How to use Sweaterdog/Andy-4 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 Sweaterdog/Andy-4 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 Sweaterdog/Andy-4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Sweaterdog/Andy-4 to start chatting

Atomic Chat new
Docker Model Runner
How to use Sweaterdog/Andy-4 with Docker Model Runner:
```
docker model run hf.co/Sweaterdog/Andy-4:Q4_K_M
```

Lemonade

How to use Sweaterdog/Andy-4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sweaterdog/Andy-4:Q4_K_M

Run and chat with the model

lemonade run user.Andy-4-Q4_K_M

List all available models

lemonade list

Andy-4 / README.md

Sweaterdog

Update README.md

0f0d905 verified about 1 year ago

4.22 kB

datasets:
  - Sweaterdog/Andy-4-base
  - Sweaterdog/Andy-4-ft
  - Sweaterdog/Andy-base-2
language:
  - en
base_model:
  - unsloth/DeepSeek-R1-Distill-Llama-8B-bnb-4bit
tags:
  - gaming
  - minecraft
  - mindcraft

🧠 Andy‑4 ⛏️

Andy‑4 is an 8 billion‑parameter specialist model tuned for Minecraft gameplay via the Mindcraft framework. Trained on a single RTX 3090 over three weeks, Andy‑4 delivers advanced reasoning, multi‑step planning, and robust in‑game decision‑making. The Current version of Andy-4 is Andy-4-0516, this was the date training finished.

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

🔍 Model Specifications

Parameters: 8 B
Training Hardware: 1 × NVIDIA RTX 3090
Duration: ~3 weeks total
Data Volumes:
- Messages: 179,384
- Tokens: 425,535,198
- Conversations: 62,149
Base Architecture: Deepseek-R1-LLaMA
License: Andy 1.1 License
Repository: https://huggingface.co/Sweaterdog/Andy‑4

📊 Training Regimen

Andy‑4‑base‑1 dataset
- Epochs: 3
- Learning Rate: 7e-5
- Dataset Size: 47.4k
Fine‑tune (FT) dataset
- Epochs: 2.5
- Learning Rate: 2e-5
- Dataset Size: 4.12k

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

🚀 Installation

First, you need to choose your quantization, this chart is with the base of 8192 set as the context window

Quantization	VRAM Required
F16	16 GB+
Q5_K_M	8 GB+
Q4_K_M	6–8 GB
Q3_K_M	6 GB (low)
Q2_K	4–6 GB (ultra)

1. Installation directly on Ollama

Visit Andy-4 on Ollama
Copy the command after choosing model type / quantization
Run the command in the terminal
Set the profile's model to be what you installed, such as ollama/sweaterdog/andy-4:latest

2. Manual Download & Modelfile

Download
- From the HF Files tab, grab your chosen .GGUF quant weights (e.g. Andy-4.Q4_K_M.gguf).
- Download the provided Modelfile.

Edit

Change

FROM YOUR/PATH/HERE

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

Optional: Increase the parameter num_ctx to a higher value for longer conversations if you:

A. Have extra VRAM

B. Quantized the context window

C. Can use a smaller model

Create
```
ollama create andy-4 -f Modelfile
```

This registers the Andy‑4 model locally.

If you lack a GPU, check the Mindcraft Discord guide for free cloud setups.

🔧 Context‑Window Quantization

To lower VRAM use for context windows:

Windows

Close Ollama.

In System Properties → Environment Variables, add:

OLLAMA_FLASH_ATTENTION=1  
OLLAMA_KV_CACHE_TYPE=q8_0   # or q4_0 for extra savings, but far more unstable

Restart Ollama.

Linux/macOS

export OLLAMA_FLASH_ATTENTION=1
export OLLAMA_KV_CACHE_TYPE="q8_0"   # or "q4_0", but far more unstable
ollama serve

📌 Acknowledgments

Click to expand

Data & Models by: @Sweaterdog
Framework: Mindcraft (https://github.com/kolbytn/mindcraft)
LoRA Weights: https://huggingface.co/Sweaterdog/Andy-4-LoRA
*Explicit credit is not granted to Meta since this model was trained off of a slightly different architecture, from DeepSeek-R1

⚖️ License

See Andy 1.1 License.

This work uses data and models created by @Sweaterdog.