README.md

---
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 ⛏️


![image/png](https://cdn-uploads.huggingface.co/production/uploads/66960602f0ffd8e3a381106a/raWYEDo2An1biTLXd5PfN.png)
**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](https://huggingface.co/Sweaterdog/Andy-4)

### Andy-4-micro models:

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

* [Andy-4-micro *(Latest Version)*](https://huggingface.co/Sweaterdog/Andy-4-micro)
* [Andy-4-micro-0427](https://huggingface.co/Sweaterdog/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)*](https://huggingface.co/Sweaterdog/Andy-4-tiny)

  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!
<details>
  <summary>Andy-4 Huggingface Install Directions</summary>

  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"

  }
  ```
</details>

## 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](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](https://ollama.com/Sweaterdog/Andy-4)
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
   ```text
   FROM YOUR/PATH/HERE
   ```
   to
   ```text
   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**  
   ```bash
   ollama create andy-4 -f Modelfile
   ```

This registers the **Andy‑4** model locally.

---

If you lack a GPU, check the [Mindcraft Discord guide](https://ptb.discord.com/channels/1303399789995626667/1347027684768878644/1347027684768878644) 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:  
   ```text
   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**

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

---

## 📌 Acknowledgments

<details>
<summary>Click to expand</summary>

- **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](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B)
</details>

---

## ⚖️ License

See [Andy 1.0 License](LICENSE).

*This work uses data and models created by @Sweaterdog.*