Create README.md

README.md

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+---
+datasets:
+- Sweaterdog/Andy-4-base-2
+- Sweaterdog/Andy-4-ft
+language:
+- en
+base_model:
+- unsloth/Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit
+tags:
+- gaming
+- minecraft
+- mindcraft
+---
+
+# 🤏 Andy‑4‑micro 🧠
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66960602f0ffd8e3a381106a/NFDS9XMy9YMM8KSn1Jjcd.png)
+
+**Andy‑4‑micro** is a lightweight Minecraft-tuned AI model derived from the Andy‑4 architecture. Built for responsiveness and portability, it’s ideal for local testing, light inference, and experimentation within the **Mindcraft** framework.
+
+**The current version of Andy-4-micro is `Andy-4-micro-0504`**, All previous versions of Andy-4-micro can still be found on my huggingface page.
+
+> 💡 Trained on a **single RTX 3070** over **four days**, Andy‑4‑micro maintains strong performance while staying efficient.
+
+> ⚠️ **Certification:**  
+> Andy‑4‑micro is **not yet certified** by the Mindcraft developers. Use in production at your own discretion.
+
+---
+
+## 📊 Model Overview
+
+- **Base Architecture:** Qwen 2.5
+- **Parameter Count:** 1.5 B
+- **Training Duration:** ~4 days
+- **Training GPU:** 1 × NVIDIA RTX 3070
+- **Total Tokens Used:** ~42M
+- **License:** [Andy 1.1 License](LICENSE)
+- **Repository:** https://huggingface.co/Sweaterdog/Andy-4-micro
+
+---
+
+## 🚀 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          | 6 GB+         |
+| Q5_K_M       | 4 GB+         |
+| Q4_K_M       | 4 GB          |
+| Q3_K_M       | 1.5 GB or CPU |
+
+**NOTE:** GPUs made before 2017 will have *significantly slower speeds* than newer GPUs, also, CPU inference will be extremely slow.
+
+### 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 & Setup
+
+1. **Download**
+   - Visit the Hugging Face **Files** tab.
+   - Download the `.GGUF` quantization weights (e.g. `Andy-4-micro.Q4_K_M.gguf`).
+   - Grab the provided `Modelfile`.
+
+2. **Edit `Modelfile`**
+
+Change the path placeholder:
+
+```text
+FROM YOUR/PATH/HERE
+```
+
+to:
+
+```text
+FROM /path/to/Andy-4-micro.Q4_K_M.gguf
+```
+
+*Optional*: Adjust `num_ctx` for longer context windows if your system supports it.
+
+3. **Create Model**
+
+```bash
+ollama create andy-4-micro -f Modelfile
+```
+
+This registers Andy‑4‑micro locally with Ollama.
+
+---
+
+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 & Model by:** @Sweaterdog  
+- **Framework:** Mindcraft (https://github.com/kolbytn/mindcraft)  
+- **LoRA Weights:** https://huggingface.co/Sweaterdog/Andy-4-micro-LoRA
+
+</details>
+
+---
+
+## ⚖️ License
+
+See [Andy 1.1 License](LICENSE).  
+
+*This work uses data and models created by @Sweaterdog.*