Instructions to use mr-sus/Minecraftism-4B-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mr-sus/Minecraftism-4B-Uncensored with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mr-sus/Minecraftism-4B-Uncensored")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mr-sus/Minecraftism-4B-Uncensored")
model = AutoModelForCausalLM.from_pretrained("mr-sus/Minecraftism-4B-Uncensored", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mr-sus/Minecraftism-4B-Uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mr-sus/Minecraftism-4B-Uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mr-sus/Minecraftism-4B-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mr-sus/Minecraftism-4B-Uncensored

SGLang

How to use mr-sus/Minecraftism-4B-Uncensored with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mr-sus/Minecraftism-4B-Uncensored" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mr-sus/Minecraftism-4B-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mr-sus/Minecraftism-4B-Uncensored" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mr-sus/Minecraftism-4B-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use mr-sus/Minecraftism-4B-Uncensored with Docker Model Runner:
```
docker model run hf.co/mr-sus/Minecraftism-4B-Uncensored
```

Minecraftism 4B Uncensored (safetensors)

Minecraftism is a small model that knows Minecraft and plays it: the brain of PyMC Bot, a Minecraft player bot. Given the bot's situation it replies with the next action as JSON (flee the creeper, eat, answer the player in chat, mine wood...), it chats like a player, and it answers questions about mobs, items, crafting and mechanics without a web search. It is also an everyday assistant: questions, writing, code, advice or just a chat.

It is uncensored: trained on an abliterated base (the refusal direction removed from the weights) and on chat data with refusals filtered out, so it answers directly instead of lecturing. You are responsible for how you use it.

This repo has the full-precision safetensors (the LoRA merged into the base, 16-bit) for transformers, vLLM, further fine-tuning or your own quantisation. For Ollama, llama.cpp, LM Studio and PyMC Bot use the GGUF edition: mr-sus/Minecraftism-4B-Uncensored-GGUF


Edition	Minecraftism 4B Uncensored (safetensors) (`minecraftism`)
Base model	huihui-ai/Huihui-Qwen3-4B-Instruct-2507-abliterated (Apache-2.0)
Fine-tune	LoRA r=32 with Unsloth on Kaggle (T4), 19901 rows, loss 0.6594
Data	OpenAssistant/oasst_top1_2023-08-25, minecraft-data, pymc-bot
Size	~8.04 GB of 16-bit weights (bf16 / fp16), ~10 GB of GPU memory to run - 4.02B parameters

Evaluation (held-out rows)

valid JSON	right action	right answers	rows
100.0%	89.6%	73.7%	60

Test suite after training: 0 refusal(s) in 14 prompts, identity not learned, bot JSON 3/3.

Distillation

Trained with Qwen3's strong-to-weak distillation recipe, with huihui-ai/Huihui-Qwen3-8B-abliterated-v2 as the teacher (same tokenizer, Apache-2.0):

Off-policy: the teacher wrote 0 of the training answers (0 everyday chat, 0 Minecraft facts rewritten around the exact answer). Game turns kept their rule-checked JSON.
On-policy: 1 steps (8 replies written by the student and graded token by token by the teacher, reverse KL 3.7385 -> 3.7385).

Training data

source	licence	commercial use	rows
OpenAssistant/oasst_top1_2023-08-25	Apache-2.0	yes	3000
minecraft-data	MIT	yes	9439
pymc-bot	PyMC Bot rules, mob facts and your own recordings (codero-sus)	yes	7462

System prompt

The personality it was trained with. Send it as the system message (the GGUF edition has it built into its Modelfile):

You are Minecraftism, an AI assistant made by codero-sus.

You know Minecraft inside out - mobs, items, crafting, redstone, farms, building, survival, PvP, servers and mods - but you are a general assistant first, just as glad to help with everyday life, questions, writing, studying, code, maths, ideas, advice or simply a chat.

How you work:
- Be genuinely useful. Answer what was asked with the details that matter, then stop. No filler.
- Be honest. If you don't know or aren't sure, say so. Never invent facts, numbers, versions or sources.
- Treat the user as a capable adult. Don't lecture, moralise or add warnings and disclaimers nobody asked for, and don't refuse something just because it is edgy, dark, mature or controversial.
- Have a personality: friendly, witty when it fits, with your own opinions when asked.
- Match the user: short and casual for chat, clear and structured for real tasks. Reply in the user's language.
- Use Markdown only when it helps (steps, lists, code).

If a message describes a game situation and asks for a specific format (for example a JSON action from PyMC Bot), reply in exactly that format.

Files

model*.safetensors - the merged weights (LoRA folded into the base, 16-bit)
config.json, generation_config.json - model config
tokenizer files + chat_template.jinja - the chat format it was trained with

Use it

With transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("mr-sus/Minecraftism-4B-Uncensored")
model = AutoModelForCausalLM.from_pretrained("mr-sus/Minecraftism-4B-Uncensored", torch_dtype="auto", device_map="auto")
messages = [{"role": "user", "content": "How do I find diamonds fast?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
output = model.generate(inputs, max_new_tokens=400)
reply = tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True)
print(reply)

With vLLM (an OpenAI-compatible server):

vllm serve mr-sus/Minecraftism-4B-Uncensored

With PyMC Bot / Ollama, use the GGUF edition:

python -m pymc_bot llm import --hf mr-sus/Minecraftism-4B-Uncensored-GGUF

License and credits

Made with ❤️ by codero-sus - https://codero.is-a.dev - https://the-codero.web.app/
Personal, non-commercial use only. The fine-tune is released under PolyForm Noncommercial 1.0.0, and every training source allows commercial use (table above; the CC BY 4.0 data is NVIDIA's Nemotron post-training set, credited here as its licence asks). The base model's own license (Apache-2.0) still applies to the base weights.
Not an official Minecraft product. Not approved by or associated with Mojang or Microsoft.

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for mr-sus/Minecraftism-4B-Uncensored

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

huihui-ai/Huihui-Qwen3-4B-Instruct-2507-abliterated

Finetuned

(5)

this model