Update README.md

README.md

@@ -5,6 +5,123 @@ base_model:
 - unsloth/Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit
 ---
 
+This is the LoRA adapter for Andy-4-micro, any use of this requires full adherence to the [Andy license](LICENSE)
+
+# 🤏 Andy-4-micro ⛏️
+
+**Andy-4-micro** is a compact and efficient model designed specifically for Minecraft gameplay via the Mindcraft framework. Built on the **Qwen2.5 1.5B** architecture, it delivers surprising capabilities given its size, offering strong reasoning and reliable task execution despite being only **21% the size** of the full Andy-4 model.
+
+Andy-4-micro is licensed under the [Andy License 1.0], which requires derivatives to include the license terms and attribute the original author.
+
+## 🧠 Overview
+
+Andy-4-micro was originally an experiment—a testbed to see how much performance could be retained when compressing the Andy-4 training process into a significantly smaller base model. The result is a fast, efficient model with an excellent ability to play Minecraft and respond with logic and clarity, even under limited hardware constraints.
+
+Despite its size, Andy-4-micro is able to:
+
+- Understand and navigate Minecraft environments.
+- Reason before responding, rather than simply reacting.
+- Play autonomously or in collaboration with user input.
+- Maintain high efficiency even on low-VRAM GPUs.
+
+
+## 📊 Training Details
+
+Andy-4-micro was trained on the same data as **Andy-4-base**, but with smaller architecture and unique training hyperparameters to optimize for compact performance.
+
+- **Base Model**: `Qwen2.5 1.5B Instruct`
+- **Total Training Epochs**: 4
+  - **Andy-4-base-2 dataset**: 2 epochs at `4e-5` learning rate
+  - **FT dataset**: 2 epochs at `8e-6` learning rate
+- **Optimizer**: AdamW with cosine decay schedule
+- **Quantization**: 4-bit (`bnb-4bit`)
+
+This configuration allows Andy-4-micro to punch well above its weight class, especially when compared with other small models in the Minecraft AI space.
+
+
+## 🔍 Differences from Andy-4-base
+
+| Feature                | Andy-4-base                     | Andy-4-micro                    |
+|------------------------|----------------------------------|---------------------------------|
+| Model Size             | 8B                               | 1.5B                            |
+| Base Architecture      | DeepSeek-R1 Distill Llama 8B     | Qwen2.5 1.5B                    |
+| Training Duration      | ~3 weeks                         | ~3 days                         |
+| Usage Type             | General Mindcraft agent base     | Fast, efficient model for light gameplay |
+| Reasoning              | High                             | Medium-high                     |
+| Hardware Requirements  | 8+                               | Very low (4-6GB VRAM works!)    |
+
+Andy-4-micro is not a replacement for Andy-4-base or the full Andy-4 model, but instead a supplementary option for users who prioritize speed and accessibility over scale.
+
+---
+
+## 🚀 Installation
+
+You can install and run Andy-4-micro through Hugging Face and Ollama, following the same method as Andy-4-base. Here's how:
+
+### 🧰 Hugging Face Quick Start
+
+1. Go to the model page on Hugging Face.
+2. Click on the **"Use this model"** dropdown.
+3. Choose **Ollama** and select your quantization level:
+
+| Quantization | Minimum VRAM Required |
+|--------------|------------------------|
+| F16          | 8+ GB                 |
+| Q5_K_M       | 6+ GB                  |
+| Q4_K_M       | 4-6 GB                 |
+| Q3_K_M       | 4 GB (low settings)    |
+| Q2_K         | 2-4 GB (ultra-light)   |
+
+4. Follow the [Mindcraft Discord Guide](https://ptb.discord.com/channels/1303399789995626667/1347027684768878644/1347027684768878644) for cloud-based options if needed.
+
+### 🛠️ Manual Installation via Modelfile
+
+1. Download your chosen `.GGUF` quantization file and the `Modelfile`.
+2. Open `Modelfile` and replace the `FROM` path with the exact path to your `.GGUF` file, e.g.:
+
+```text
+FROM C:\Users\you\Downloads\Andy-4-micro.Q3_K_M.gguf
+```
+
+3. In a terminal, navigate to the `Modelfile` directory and run:
+
+```bash
+ollama create andy-4-micro -f Modelfile
+```
+
+This will register the model as `andy-4-micro` for use with Ollama.
+
+---
+
+## 🧪 Experimental Nature
+
+> **Note**: Andy-4-micro is an experimental release.
+
+Although initial performance was overwhelmingly positive, Andy-4-micro was not originally designed for public deployment. However, due to strong community feedback and impressive early benchmarks, it has been released for broader use and further testing.
+
+Expect updates, bugfixes, and potentially further fine-tuned versions based on user reports.
+
+---
+
+## ⚖️ License
+
+This model is licensed under the **Andy License 1.0**.  
+Usage must include credit to the original creator, and derivatives must remain open and under the same license.
+
+- Author: [Sweaterdog](https://huggingface.co/Sweaterdog)
+- Acknowledgments: This model utilizes datasets and techniques developed in the Andy-4 ecosystem.
+- License type: Derived from Apache 2.0 with added attribution & openness clauses.
+
+
+## 📌 Final Notes
+
+- Andy-4-micro is ideal for **lightweight deployments**, **educational use**, and **embedded inference**.
+- It’s a great fit for users who want a responsive, low-resource model that still understands Minecraft deeply.
+- For best results, use within the Mindcraft framework or alongside other Andy models.
+
+We hope you enjoy exploring and building with **Andy-4-micro**!
+
+> _"Big things come in small packages."_ 💡
 
 ```
 ## License