license: other
license_name: polyform-noncommercial-1.0.0
license_link: https://polyformproject.org/licenses/noncommercial/1.0.0/
base_model: huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated
tags:
- minecraft
- safetensors
- pymc-bot
- minecraftism
- qwen3
- unsloth
- game-agent
- reasoning
- thinking
- uncensored
- abliterated
language: - en
pipeline_tag: text-generation
library_name: transformers
Minecraftism 4B Thinking 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 thinks first: before every answer it writes a short <think> about its health, the mobs around it (with facts from the mob database), chat and its goal, then the JSON action. Smarter choices, slower turns.
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-Thinking-Uncensored-GGUF
| Edition | Minecraftism 4B Thinking Uncensored (safetensors) (minecraftism-thinking) |
| Base model | huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated (Apache-2.0) |
| Fine-tune | LoRA r=32 with Unsloth on Kaggle (T4), 16917 rows, loss 0.0539 |
| Data | minecraft-data, pymc-bot, nvidia/Nemotron-Post-Training-Dataset-v1 |
| 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% | 68.8% | 26.3% | 60 |
Test suite after training: 0 refusal(s) in 14 prompts, identity 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, with its own thinking traces). Game turns kept their rule-checked JSON.
- On-policy: 60 steps (480 replies written by the student and graded token by token by the teacher, reverse KL 3.4609 -> 0.7968).
Training data
| source | licence | commercial use | rows |
|---|---|---|---|
| minecraft-data | MIT | yes | 9439 |
| pymc-bot | PyMC Bot rules, mob facts and your own recordings (codero-sus) | yes | 7462 |
| nvidia/Nemotron-Post-Training-Dataset-v1 | CC BY 4.0 | yes | 16 |
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-Thinking-Uncensored")
model = AutoModelForCausalLM.from_pretrained("mr-sus/Minecraftism-4B-Thinking-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-Thinking-Uncensored
With PyMC Bot / Ollama, use the GGUF edition:
python -m pymc_bot llm import --hf mr-sus/Minecraftism-4B-Thinking-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.