cc-coder — a CC: Tweaked (ComputerCraft) Lua coding model

A QLoRA adapter for Qwen2.5-Coder-3B-Instruct specialized in writing CC: Tweaked Lua programs for Minecraft — turtles, peripherals, rednet, monitors, and the wider CC library ecosystem — while retaining general programming ability.

Trained entirely on a single RTX 5060 (8GB) using Unsloth, and iterated against an executable benchmark: every evaluation generation is run inside a real CraftOS-PC emulator with syntax + runtime checking, and every hand-written training example was validated the same way before it was allowed into the dataset.

Evaluation (executable, in-emulator)

70 held-out prompts; a pass = the generated program parses and runs cleanly in CraftOS-PC (with stub turtle API and installed community libs):

Metric	Training-loop start (iter 1)	This model (iter 9)
Everyday CC tasks (50 prompts, clean-run %)	44%	74%
Full set incl. library tasks (70 prompts)	—	60%
Syntax-valid generations	94%	97%

(3 of the 20 library prompts are unwinnable in the harness — broken vendored dependency, internet-requiring font, nonexistent module — so the effective library ceiling is 17/20.)

Training data

• The CC: Tweaked ROM, official docs, and ~130 community repositories (Basalt, Pine3D, PixelUI, Opus OS, artist, ccryptolib, metis, and many more), each file validated in the emulator before inclusion; licenses of source repos include MPL-2.0, MIT, and LicenseRef-CCPL
• 112 hand-written, emulator-validated canonical examples targeting observed failure modes (nil-guards, yield-correct event loops, exact API signatures, library require idioms)
• ~5,000 general programming samples (Magicoder-OSS-Instruct) to retain non-Lua skills

Usage

With PEFT / transformers (the adapter also loads onto the full-precision Qwen/Qwen2.5-Coder-3B-Instruct base):

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base, "minecartchris/cc-coder")
tokenizer = AutoTokenizer.from_pretrained("minecartchris/cc-coder")

Prompt in plain chat format, e.g. "Write a CC: Tweaked turtle program that digs a 1x2 tunnel 16 blocks long and returns home."

A GGUF build (q4_k_m, runs in Ollama / LM Studio on ~2GB) is planned as a companion repo.

Limitations

• 3B parameters: logic on complex multi-step tasks can be wrong even when the code runs; always review before letting a turtle loose on your base
• Library coverage (Basalt, Pine3D, etc.) is functional but weaker than vanilla-API coverage
• Inherits the Qwen Research license from its base model (non-commercial restrictions apply — see license link)