README.md

---
tags:
- text-generation
- fine-tuned
- deepseek-coder
- spigot
- minecraft
- code-generation
- instruct
- gguf
- ollama
datasets:
- custom_spigot_dataset # You can change this if your dataset has a public Hugging Face ID
license: apache-2.0 # Assuming Apache 2.0, common for DeepSeek. Please verify or change.
model-index:
- name: LunarAI
  results: [] # You can add evaluation results here if you have them
---

# 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!")