Instructions to use chriscelaya/minecraft-ai-training-tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chriscelaya/minecraft-ai-training-tutorial with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("chriscelaya/minecraft-ai-training-tutorial", dtype="auto")

llama-cpp-python

How to use chriscelaya/minecraft-ai-training-tutorial with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="chriscelaya/minecraft-ai-training-tutorial",
	filename="unsloth.F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use chriscelaya/minecraft-ai-training-tutorial with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf chriscelaya/minecraft-ai-training-tutorial:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf chriscelaya/minecraft-ai-training-tutorial:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf chriscelaya/minecraft-ai-training-tutorial:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf chriscelaya/minecraft-ai-training-tutorial:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf chriscelaya/minecraft-ai-training-tutorial:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf chriscelaya/minecraft-ai-training-tutorial:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf chriscelaya/minecraft-ai-training-tutorial:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chriscelaya/minecraft-ai-training-tutorial:Q4_K_M

Use Docker

docker model run hf.co/chriscelaya/minecraft-ai-training-tutorial:Q4_K_M

LM Studio
Jan
Ollama
How to use chriscelaya/minecraft-ai-training-tutorial with Ollama:
```
ollama run hf.co/chriscelaya/minecraft-ai-training-tutorial:Q4_K_M
```

Unsloth Studio

How to use chriscelaya/minecraft-ai-training-tutorial with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for chriscelaya/minecraft-ai-training-tutorial to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for chriscelaya/minecraft-ai-training-tutorial to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for chriscelaya/minecraft-ai-training-tutorial to start chatting

Docker Model Runner
How to use chriscelaya/minecraft-ai-training-tutorial with Docker Model Runner:
```
docker model run hf.co/chriscelaya/minecraft-ai-training-tutorial:Q4_K_M
```

Lemonade

How to use chriscelaya/minecraft-ai-training-tutorial with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chriscelaya/minecraft-ai-training-tutorial:Q4_K_M

Run and chat with the model

lemonade run user.minecraft-ai-training-tutorial-Q4_K_M

List all available models

lemonade list

chriscelaya commited on Jan 22, 2025

Commit

57e5877

verified ·

1 Parent(s): ebca243

Update README.md

Browse files

Files changed (1) hide show

README.md +17 -1

README.md CHANGED Viewed

@@ -16,6 +16,7 @@ language:
 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)
@@ -34,6 +35,7 @@ This **readme.md** provides step-by-step instructions to:
 ---
 ### 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.
@@ -42,6 +44,7 @@ This **readme.md** provides step-by-step instructions to:
 ---
 ## 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.
@@ -156,6 +159,7 @@ 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.
@@ -164,4 +168,16 @@ model.save_pretrained("andy_minecraft_assistant")
 For more details on **Unsloth** or to contribute, visit [Unsloth GitHub](https://github.com/unslothai/unsloth).
-Happy fine-tuning! 🎮

 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)
 ---
 ### 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.
 ---
 ## 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.
 ---
 ## 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}}
+}