Instructions to use Sweaterdog/MindCraft-LLM-tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sweaterdog/MindCraft-LLM-tuning with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Sweaterdog/MindCraft-LLM-tuning", dtype="auto")

llama-cpp-python

How to use Sweaterdog/MindCraft-LLM-tuning with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Sweaterdog/MindCraft-LLM-tuning",
	filename="Andy-3.5-beta.F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Sweaterdog/MindCraft-LLM-tuning with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Sweaterdog/MindCraft-LLM-tuning:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Sweaterdog/MindCraft-LLM-tuning:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Sweaterdog/MindCraft-LLM-tuning:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Sweaterdog/MindCraft-LLM-tuning: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 Sweaterdog/MindCraft-LLM-tuning:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Sweaterdog/MindCraft-LLM-tuning: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 Sweaterdog/MindCraft-LLM-tuning:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sweaterdog/MindCraft-LLM-tuning:Q4_K_M

Use Docker

docker model run hf.co/Sweaterdog/MindCraft-LLM-tuning:Q4_K_M

LM Studio
Jan
Ollama
How to use Sweaterdog/MindCraft-LLM-tuning with Ollama:
```
ollama run hf.co/Sweaterdog/MindCraft-LLM-tuning:Q4_K_M
```

Unsloth Studio

How to use Sweaterdog/MindCraft-LLM-tuning 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 Sweaterdog/MindCraft-LLM-tuning 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 Sweaterdog/MindCraft-LLM-tuning to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Sweaterdog/MindCraft-LLM-tuning to start chatting

Docker Model Runner
How to use Sweaterdog/MindCraft-LLM-tuning with Docker Model Runner:
```
docker model run hf.co/Sweaterdog/MindCraft-LLM-tuning:Q4_K_M
```

Lemonade

How to use Sweaterdog/MindCraft-LLM-tuning with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sweaterdog/MindCraft-LLM-tuning:Q4_K_M

Run and chat with the model

lemonade run user.MindCraft-LLM-tuning-Q4_K_M

List all available models

lemonade list

Sweaterdog commited on Jan 28, 2025

Commit

fb25d4e

verified ·

1 Parent(s): c296c68

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -13

README.md CHANGED Viewed

@@ -22,6 +22,10 @@ datasets:
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/Qwen2.5-7B-bnb-4bit and unsloth/Llama-3.2-3B-Instruct
 The MindCraft LLM tuning CSV file can be found here, this can be tweaked as needed. [MindCraft-LLM](https://huggingface.co/datasets/Sweaterdog/MindCraft-LLM-tuning)
 # What is the Purpose?
@@ -69,8 +73,9 @@ In order to use this model, A, download the GGUF file of the version you want, e
 9. (Optional, needed for versions after the 11/15/24 update) If you downloaded a model that was tuned from Qwen, and in the model name you kept Qwen, you need to go into the file "prompter.js" and remove the qwen section, if you named it something that doesn't include qwen in the name, you can skip this step.
-# How to fine tune a Gemini Model
-1. Download the CSV for [MindCraft-LLM-tuning](https://huggingface.co/datasets/Sweaterdog/MindCraft-LLM-tuning)
 2. Open sheet.google.com, and upload the CSV file
 3. Go to [API keys and Services](https://aistudio.google.com/app/apikey), then click on "New Tuned Model" on the left popup bar
 4. Press "Import" and then select the CSV file you uploaded to google sheets
@@ -93,9 +98,6 @@ echo Restarting...
 goto loop
 ```
 12. Enjoy having a model play Minecraft with you, hopefully it is smarter than regular Gemini models!
-#
-**WARNING** The new v3 generation of models suck! That is because they were also trained for building *(coding)* and often do not use commands! I recommend using the v2 generation still, the LLaMa version is in the [deprecated models folder](https://huggingface.co/Sweaterdog/MindCraft-LLM-tuning/tree/main/deprecated-models).
 #
@@ -103,14 +105,7 @@ For Anybody who is wondering what the context length is, the Qwen version, has a
 #
-I wanted to include the google colab link, in case you wanted to know how to train models via CSV, or use my dataset to train your own model, on your own settings, on a different model. [Google Colab](https://colab.research.google.com/drive/1VYkncZMfGFkeCEgN2IzbZIKEDkyQuJAS#scrollTo=2eSvM9zX_2d3)
 #
 **UPDATE** The Qwen and Llama models are out, with the expanded dataset! I have found the llama models are incredibly dumb, but changing the Modelfile may provide better results, With the Qwen version of Andy, the Q4_K_M, it took 2 minutes to craft a wooden pickaxe, collected stone after that, took 5 minutes.
-#
-This qwen2 and llama3.2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/Qwen2.5-7B-bnb-4bit and unsloth/Llama-3.2-3B-Instruct
+# THIS IS A DEPRECATED REPOSITORY, DO NOT USE MODELS FROM THIS REPO, INSTEAD, [FIND THE NEWER AND MUCH BETTER MODELS HERE](https://huggingface.co/Sweaterdog/Andy-3.5)
+## AGAIN, THIS REPO IS ONLY FOR TESTING OLD MODELS
 The MindCraft LLM tuning CSV file can be found here, this can be tweaked as needed. [MindCraft-LLM](https://huggingface.co/datasets/Sweaterdog/MindCraft-LLM-tuning)
 # What is the Purpose?
 9. (Optional, needed for versions after the 11/15/24 update) If you downloaded a model that was tuned from Qwen, and in the model name you kept Qwen, you need to go into the file "prompter.js" and remove the qwen section, if you named it something that doesn't include qwen in the name, you can skip this step.
+# How to fine tune a Gemini Model
+### *--This is no longer supported in newer dataset versions*
+1. Download the CSV for [MindCraft-LLM-tuning](https://huggingface.co/datasets/Sweaterdog/MindCraft-LLM-tuning) *--Deprecated dataset*
 2. Open sheet.google.com, and upload the CSV file
 3. Go to [API keys and Services](https://aistudio.google.com/app/apikey), then click on "New Tuned Model" on the left popup bar
 4. Press "Import" and then select the CSV file you uploaded to google sheets
 goto loop
 ```
 12. Enjoy having a model play Minecraft with you, hopefully it is smarter than regular Gemini models!
 #
 #
 #
 **UPDATE** The Qwen and Llama models are out, with the expanded dataset! I have found the llama models are incredibly dumb, but changing the Modelfile may provide better results, With the Qwen version of Andy, the Q4_K_M, it took 2 minutes to craft a wooden pickaxe, collected stone after that, took 5 minutes.
+**UPDATE** DO NOT USE THE MODELS FROM THIS REPO, IT TOOK ~3:00.00 TO GET A STONE PICKAXE, MUCH FASTER THAN ANDY-V2-QWEN