README.md

---
base_model: unsloth/Qwen2.5-7B-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---

# Efficient Fine-Tuning of Large Language Models - Minecraft AI Assistant Tutorial  

This repository demonstrates how to fine-tune the **Qwen 7B** model to create "Andy," an AI assistant for Minecraft. Using the **Unsloth framework**, this tutorial showcases efficient fine-tuning with 4-bit quantization and LoRA for scalable training on limited hardware.

## 🚀 Resources  

- **Source Code**: [GitHub Repository](https://github.com/while-basic/mindcraft)
- **Colab Notebook**: [Colab Notebook](https://colab.research.google.com/drive/1Eq5dOjc6sePEt7ltt8zV_oBRqstednUT?usp=sharing)
- **Blog Article**: [Walkthrough](https://chris-celaya-blog.vercel.app/articles/unsloth-training)
- **Dataset**: [Andy-3.5](https://huggingface.co./datasets/Sweaterdog/Andy-3.5)
- **Teaser**: [Video](https://www.youtube.com/watch?v=KUXY5OtaPZc)
  
## Overview  

This **readme.md** provides step-by-step instructions to:  
1. Install and set up the **Unsloth framework**.  
2. Initialize the **Qwen 7B** model with **4-bit quantization**.  
3. Implement **LoRA Adapters** for memory-efficient fine-tuning.  
4. Prepare the **Andy-3.5 dataset** with Minecraft-specific knowledge.  
5. Configure and execute training in a resource-efficient manner.  
6. Evaluate and deploy the fine-tuned AI assistant.

---

### Key Features  

- **Memory-Efficient Training**: Fine-tune large models on GPUs as low as T4 (Google Colab).  
- **LoRA Integration**: Modify only key model layers for efficient domain-specific adaptation.  
- **Minecraft-Optimized Dataset**: Format data using **ChatML templates** for seamless integration.  
- **Accessible Hardware**: Utilize cost-effective setups with GPU quantization techniques.

---

## Prerequisites  

- **Python Knowledge**: Familiarity with basic programming concepts.  
- **GPU Access**: T4 (Colab Free Tier) is sufficient; higher-tier GPUs like V100/A100 recommended.  
- **Optional**: [Hugging Face Account](https://huggingface.co./) for model sharing.

---

## Setup  

Install the required packages:  
```bash
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --no-deps xformers trl peft accelerate bitsandbytes
```

---

## Model Initialization  

Load the **Qwen 7B** model with 4-bit quantization for reduced resource usage:  

```python
from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Qwen2.5-7B-bnb-4bit",
    max_seq_length=2048,
    dtype=torch.bfloat16,
    load_in_4bit=True,
    trust_remote_code=True,
)
```

---

## Adding LoRA Adapters  

Add LoRA to fine-tune specific layers efficiently:  
```python
model = FastLanguageModel.get_peft_model(
    model,
    r=16,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "embed_tokens", "lm_head"],
    lora_alpha=16,
    lora_dropout=0,
    use_gradient_checkpointing="unsloth",
)
```

---

## Dataset Preparation  

Prepare the Minecraft dataset (**Andy-3.5**):  
```python
from datasets import load_dataset
from unsloth.chat_templates import get_chat_template

dataset = load_dataset("Sweaterdog/Andy-3.5", split="train")
tokenizer = get_chat_template(tokenizer, chat_template="chatml")
```

---

## Training Configuration  

Set up the training parameters:  
```python
from trl import SFTTrainer
from transformers import TrainingArguments

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="text",
    args=TrainingArguments(
        per_device_train_batch_size=16,
        max_steps=1000,
        learning_rate=2e-5,
        gradient_checkpointing=True,
        output_dir="outputs",
        fp16=True,
    ),
)
```

Clear unused memory before training:  
```python
import torch
torch.cuda.empty_cache()
```

---

## Train the Model  

Initiate training:  
```python
trainer_stats = trainer.train()
```

---

## Save and Share  

Save your fine-tuned model locally or upload to Hugging Face:  
```python
model.save_pretrained("andy_minecraft_assistant")
```

---

## Optimization Tips  

- Expand the dataset for broader Minecraft scenarios.  
- Adjust training steps for better accuracy.  
- Fine-tune inference parameters for more natural responses.

---

For more details on **Unsloth** or to contribute, visit [Unsloth GitHub](https://github.com/unslothai/unsloth).  

Happy fine-tuning! 🎮

## Citation

@misc{celaya2025minecraft,
  author = {Christopher B. Celaya},
  title = {Efficient Fine-Tuning of Large Language Models - A Minecraft AI Assistant Tutorial},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/kolbytn/mindcraft}},
  note = {\url{https://chris-celaya-blog.vercel.app/articles/unsloth-training}}
}