Create README.md

README.md

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+---
+datasets:
+- Sweaterdog/Andy-4-base
+- Sweaterdog/Andy-4-ft
+- Sweaterdog/Andy-base-2
+language:
+- en
+base_model:
+- HuggingFaceTB/SmolLM2-360M-Instruct
+tags:
+- gaming
+- minecraft
+- mindcraft
+---
+
+# 🧠 Andy‑4-tiny 🐜
+
+
+**Andy‑4-tiny** is an 360 Million‑parameter specialist model tuned for Minecraft gameplay via the Mindcraft framework.
+**The Current version of Andy-4-tiny is** `Andy-4-tiny-0522`.
+
+> ⚠️ **Certification:**  
+> Andy‑4 is **not yet certified** by the Mindcraft developers. Use in production at your own discretion.
+
+
+## 🔍 Model Specifications
+
+- **Parameters:** 360M  
+- **Training Hardware:** 1 × NVIDIA RTX 3070  
+- **Duration:** ~ 36 hours total  
+- **Data Volumes:**  
+  - **Messages:** 179,384  
+  - **Tokens:** 425,535,198  
+  - **Conversations:** 62,149  
+
+- **Base Architecture:** SmolLM2 
+- **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:**   5e-5
+   - **Dataset Size:** 47.4k
+
+2. **Andy‑4‑base-2** dataset  
+   - **Epochs:** 2  
+   - **Learning Rate:**   7e-5
+   - **Dataset Size:** 49.2k
+
+3. **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
+
+Andy-4-tiny is an Edge-case model, built to run on the CPU and use minimal ram
+
+| Quantization | RAM Required |
+|--------------|---------------|
+| F16          | CPU        |
+| Q8_0         | CPU        |
+| Q4_K_M       | CPU        |
+
+### 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:tiny-q8_0`
+
+### 2. Manual Download & Modelfile
+
+1. **Download**  
+   - From the HF **Files** tab, grab your chosen `.GGUF` quant weights (e.g. `Andy-4-tiny.Q4_K_M.gguf`).  
+   - Download the provided `Modelfile`.
+
+
+2. **Edit**
+
+   Change
+   ```text
+   FROM YOUR/PATH/HERE
+   ```
+   to
+   ```text
+   FROM /path/to/Andy-4-tiny.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-tiny -f Modelfile
+   ```
+
+This registers the **Andy‑4-tiny** model locally.
+
+---
+
+## 📌 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.*