Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- en
+base_model: google/gemma-2
+pipeline_tag: image-text-to-text
+tags:
+- gemma
+- gguf
+- vision
+- llama.cpp
+- quantized
 ---
+
+# 🏔️ Winter LLM (Gemma Multimodal GGUF)
+
+Welcome to **Winter LLM**, a lightweight, high-performance GGUF build based on Google's Gemma architecture with multimodal (vision + text) capabilities. Designed for fast, efficient local execution on consumer hardware, PCs, and edge setups across any region—from Kigali to anywhere in the world! 🌍✨
+
+---
+
+## 🚀 Overview
+
+* **Base Architecture:** Gemma Multimodal
+* **Format:** Ready-to-run `.gguf`
+* **Features:** 
+  * 💬 **Text Generation & Chat:** Fast local inference.
+  * 👁️ **Vision Capability:** Powered by the included `mmproj` vision adapter for reading and understanding images.
+  * ⚡ **Optimized Memory Usage:** Quantized to run efficiently on low-VRAM GPUs or standard CPUs.
+
+---
+
+## 📂 Included Files
+
+| File Name | Size | Purpose |
+| :--- | :--- | :--- |
+| `winter llm.gguf` | ~6 GB | Main LLM model weights & tokenizer |
+| `mmproj-gemma-4-12B-it-QAT-BF16.gguf` | ~100 MB | Multimodal vision projector (for image inputs) |
+
+---
+
+## 💻 How to Run Locally
+
+You can run **Winter LLM** offline using any GGUF-compatible software:
+
+### 1. LM Studio / Jan AI
+1. Download both `winter llm.gguf` and the `mmproj-...gguf` file from the **Files** tab.
+2. Drag and drop `winter llm.gguf` into LM Studio.
+3. Attach the `mmproj` projector in settings if you wish to upload images to the chat!
+
+### 2. Ollama / llama.cpp
+Run via terminal using `llama.cpp`:
+```bash
+./main -m "winter llm.gguf" --mmproj "mmproj-gemma-4-12B-it-QAT-BF16.gguf" -p "User: Describe this image: [image] Assistant:"