Sweaterdog/Andy-4

🧠 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.

---

This is a general model repo, any other models will be listed below:

Andy-4 models:

(Good all around model for anyone with less than 16GB of VRAM)

• This Repo

Andy-4-micro models:

(Great model to fit inside of laptops or low-end PCs)

• Andy-4-micro (Latest Version)
• Andy-4-micro-0427

Andy-4-tiny models:

(Generally not recommended due to low performance, but great for edge-case scenarios like phones)

• Andy-4-tiny (Not released)

  Andy-4-tiny has yet to be released, but is in training

---

If you are downloading on Huggingface, follow these directions!

DO NOT Use the Use This Model feature in Huggingface!

Andy-4 Huggingface Install Directions

Method One:

1. Select the model you would like to use

2. Download the Modelfile

3. Once downloaded, open Modelfile in a text editor, and change the FROM parameter from YOUR/PATH/HERE to the download location of the gguf file, this has to be exact!

4. When changed, save the file, and open command terminal

5. (Optional if CMD isn't opened via file explorer) Navigate to the correct directory using "cd"

6. Run the command ollama create sweaterdog/Andy-4 -f Modelfile If you want multiple models, include a tag afterwards. Example: sweaterdog/Andy-4:micro-fp16 or sweaterdog/Andy-4:q2_k

7. Go to a profile in MindCraft

8. Change the model to be sweaterdog/Andy-4 Or whatever you named your model

9. Ensure you have the emdedding tag set to Ollama, like below

{
  "name": "andy-4",

  "model": "Sweaterdog/Andy-4",

  "embedding": "ollama"

}

Method Two:

1. Download the Modelfile

2. Once downloaded, open Modelfile in a text editor, and change the FROM parameter from YOUR/PATH/HERE To one of the models listed here in the Use This Model tab under ollama, here are the options: ```

hf.co/Sweaterdog/Andy-4:Q2_K

hf.co/Sweaterdog/Andy-4:Q3_K_M

hf.co/Sweaterdog/Andy-4:Q4_K_M

hf.co/Sweaterdog/Andy-4:Q5_K_M

hf.co/Sweaterdog/Andy-4:Q8_0

hf.co/Sweaterdog/Andy-4:F16

3. When changed, save the file, and open command terminal

4. (Optional if CMD isn't opened via file explorer) Navigate to the correct directory using "cd"

5. Run the command ollama create sweaterdog/Andy-4 -f Modelfile If you want multiple models, include a tag afterwards. Example: sweaterdog/Andy-4:micro-fp16 or sweaterdog/Andy-4:q2_k

6. Go to a profile in MindCraft

7. Change the model to be sweaterdog/Andy-4 Or whatever you named your model

8. Ensure you have the emdedding tag set to Ollama, like below

{
  "name": "andy-4",

  "model": "Sweaterdog/Andy-4",

  "embedding": "ollama"

}

DO NOT SKIP THIS SECTION IF YOU INTEND ON INSTALLING OFF OF HUGGINGFACE

---

🔍 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 1.0 License

• Repository: https://huggingface.co/Sweaterdog/Andy‑4

---

📊 Training Regimen

1. Andy‑4‑base‑1 dataset

   • Epochs: 2
   • Learning Rate: 4e-5
   • Dataset Size: 47.4k

2. Andy‑4‑base-2 dataset

   • Epochs: 2.5
   • Learning Rate: 7e-5
   • Dataset Size: 49.2k

3. Fine‑tune (FT) dataset

   • Epochs: 1
   • 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	20 GB+
Q8_0	12 GB
Q5_K_M	8 GB+
Q4_K_M	6–8 GB
Q3_K_M	6 GB (low)
Q2_K	4–6 GB (ultra low)

1. Installation directly on Ollama

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

2. Manual Download & Modelfile

1. Download

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

2. Edit

   Change

   FROM YOUR/PATH/HERE
   

   to

   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

3. 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

1. Close Ollama.
2. 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
   

3. 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 1.0 License.

This work uses data and models created by @Sweaterdog.