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+# LunarAI - Fine-tuned DeepSeek Coder V2 Lite for Spigot Plugin Development
+
+## Model Description
+
+**LunarAI** is a custom language model fine-tuned from the `deepseek-ai/DeepSeek-Coder-V2-Lite-Base` model. It has been specialized to act as an AI programming assistant, with a particular focus on **Spigot/Minecraft plugin development**.
+
+This model is designed to provide accurate code examples, explanations, and guidance related to the Spigot API and general Java programming concepts relevant to creating Minecraft server plugins.
+
+## Training Details
+
+* **Base Model:** `deepseek-ai/DeepSeek-Coder-V2-Lite-Base`
+* **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+* **Dataset:** A custom dataset (`spigot_dataset.jsonl`) focused on Spigot/Minecraft plugin development, including common tasks, event handling, and API usage.
+* **Adapter Size:** Approximately 1.1 GB (LoRA adapter before merge)
+* **Training Framework:** Axolotl
+
+## Model Files
+
+This repository contains two main versions of the fine-tuned model:
+
+1.  **Full Merged Model (Safetensors):** The complete model with the LoRA adapter merged into the base model's weights. This is the standard Hugging Face format, ideal for further development or use with `transformers`.
+    * Files: `model-00001-of-00007.safetensors` through `model-00007-of-00007.safetensors` (totaling ~31.4 GB)
+    * Configuration files: `config.json`, `tokenizer.json`, `special_tokens_map.json`, etc.
+
+2.  **Quantized GGUF Model (for Ollama):** A highly optimized, quantized version of the merged model in GGUF format, specifically designed for efficient local inference with tools like Ollama.
+    * File: `model.gguf` (~16.7 GB, `q8_0` quantization)
+
+## How to Use LunarAI with Ollama (Recommended for Local Inference)
+
+To run **LunarAI** locally using Ollama, follow these steps:
+
+1.  **Ensure Ollama is Installed:** If you don't have Ollama, install it from [ollama.com](https://ollama.com/).
+2.  **Download `model.gguf`:** You can download the `model.gguf` file directly from this repository's "Files" tab, or use `ollama pull ThePegasusGroup/LunarAI` if Ollama supports direct pulling of GGUF files from the Hub (this might require a `Modelfile` first).
+3.  **Create a `Modelfile`:** In the same directory as your downloaded `model.gguf`, create a file named `Modelfile` with the following content:
+
+    ```dockerfile
+    # Tell Ollama which GGUF file to use
+    FROM ./model.gguf
+
+    # Set the chat template for DeepSeek Coder
+    TEMPLATE """{% for message in messages %}{% if message['role'] == 'user' %}{{ 'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company. Follow the user\'s instructions carefully. Respond using markdown.' }}\n### Instruction:\n{{ message['content'] }}\n### Response:\n{% elif message['role'] == 'assistant' %}{{ message['content'] }}{% if not loop.last %}\n{% endif %}{% endif %}{% endfor %}"""
+
+    # Set a default parameter
+    PARAMETER temperature 0.7
+    ```
+
+4.  **Create the Model in Ollama:**
+    ```bash
+    ollama create LunarAI -f ./Modelfile
+    ```
+
+5.  **Run LunarAI:**
+    ```bash
+    ollama run LunarAI
+    ```
+    You can then start asking it questions related to Spigot plugin development!
+
+## How to Load the Merged Model with Hugging Face Transformers
+
+If you wish to load the full, unquantized merged model for further development or advanced usage with the `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Replace 'ThePegasusGroup/LunarAI' with the actual repo ID if you renamed it
+model_id = "ThePegasusGroup/LunarAI"
+
+# Load the model
+# Ensure you have sufficient VRAM (GPU memory) or RAM for this large model
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16, # Or torch.float16, or torch.float32 depending on your hardware
+    device_map="auto",
+    trust_remote_code=True # Required for DeepSeek-Coder-V2-Lite-Base architecture
+)
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+
+print("LunarAI model loaded successfully!")