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Additive Manufacturing Reasoning dataset

As explained in Domain-Specific Small Language Models

A dataset of additive-manufacturing questions, answers, and reasoning traces used to train and evaluate the example model.

A 10,000-sample dataset of additive-manufacturing questions, answers, and reasoning traces. It supports training and evaluating a domain-specific small language model with grounded prompts and reference answers. section 15.4

Its schema and preparation steps are straightforward:

  • question supplies the prompt content for training. section 15.4
  • answer serves as the ground truth target. section 15.4
  • reason stores the reasoning used to produce each answer, but it is removed after prompt construction. section 15.4

The dataset is used by loading the training split, mapping questions into a chat template, and dropping unneeded columns before splitting into train and test sets. section 15.4

Prompt and target preparation

A system message requires <reasoning> and <answer> tags, while the user prompt asks for an additive-manufacturing answer and tells the model to review the question carefully.

How Domain-Specific Small Language Models covers it

How deep it goes
Defines the columns, shows prompt preparation, and stops short of detailing dataset curation beyond sampling and splitting.
Examples
Uses additive-manufacturing questions, a system format with reasoning and answer tags, and a sample prompt about simulation simplification.
What it assumes
Assumes basic chat-style prompt formatting and supervised dataset handling, while teaching the dataset columns from scratch.
What you can do after
Apply it to train and evaluate a small model on additive-manufacturing reasoning tasks.
How it connects
Connects to GRPO, LoRA, QLoRA, reward functions, and chat templates.
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