NullvoidXO/elegoo-resin-printing-v1

Elegoo Resin Printing Dataset v1

A specialized dataset for fine-tuning language models on 3D resin printing with Elegoo printers.

Description

This dataset contains training examples for building AI assistants that can:

• Recommend optimal slicer settings (exposure, lift speed, layer height) for specific printer/resin combinations
• Diagnose print failures and provide actionable fixes
• Generate ChiTuBox/Lychee slicer configuration files

Dataset Composition

Source	Count	Description
Synthetic (settings)	2,447	Slicer settings recommendations grounded in scraped docs
Synthetic (diagnosis)	2,435	Failure diagnosis Q&A
Synthetic (config)	2,393	Slicer config file generation (ChiTuBox/Lychee)
Synthetic (Mercury)	143	Wash/cure station diagnosis (Mercury Plus V2 focus)
Real Q&A	109	StackExchange resin printing questions
Doc chunks	344	Converted documentation excerpts
Total	7,869

Priority Coverage

A special focus was applied to two of the most commonly used products:

• Mars 4 Ultra: 3x weight in synthetic generation (~18% of printer mentions)
• Mercury Plus V2: 150 diagnosis examples (wash/cure failures)

Format

Conversational format compatible with SFT training:

### Human:
{instruction/input}

### Assistant:
{output}

Intended Use

Fine-tuning language models (e.g., Qwen3.5-9B, Llama 3.1 8B) for:

• Resin printing assistance
• Slicer settings optimization
• Print failure diagnosis

Data Sources

Scraped (used for grounding)

• Elegoo Wiki (wiki.elegoo.com)
• ChiTuBox Documentation (docs.chitubox.com)
• Lychee/Mango3D Documentation (docs.mango3d.io)
• 3DPrinting StackExchange (via HuggingFace dataset)

Synthetic Generation

Generated using Ollama Cloud API (qwen3-coder-next:cloud) with doc-grounded prompting.

License

• Synthetic data: Apache 2.0
• Scraped documentation: Original licenses retained (Elegoo, ChiTuBox, Mango3D)
• StackExchange: CC BY-SA 4.0

Training Script

Compatible with standard SFT pipelines:

from datasets import load_dataset
from transformers import AutoTokenizer

dataset = load_dataset("your-username/elegoo-resin-printing-v1")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")

def tokenize(example):
    return tokenizer(example["text"], truncation=True, max_length=2048)

tokenized = dataset.map(tokenize)

Version History

• v1 (2026-04-17): Initial release, 7,869 examples
  • Text-based V1 scope (settings, diagnosis, config)
  • V2 features deferred (image-to-3D, YouTube transcripts)

Citation

@dataset{elegoo-resin-printing-v1,
  author = {Your Name},
  title = {Elegoo Resin Printing Dataset v1},
  year = {2026},
  url = {https://huggingface.co/datasets/your-username/elegoo-resin-printing-v1}
}