README.md

---
# ============================================================================
# DATASET CARD FRONT-MATTER (YAML)
# Confirm the license before publishing (see Licensing section).
# ============================================================================
license: cc-by-nc-4.0               # TODO CONFIRM: derived work in a private fictional universe; choose your terms
pretty_name: For The Small Shield — Instruction Data
language:
  - en
task_categories:
  - text-generation
tags:
  - for-the-small-shield
  - rise-and-set
  - roleplay
  - storytelling
  - instruction-tuning
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
---

# For The Small Shield — Instruction Data

The training data to fine-tune an LLM is derived from a 1.2-million-word 
manuscript called For The Small Shield (https://github.com/wordsum/For_The_Small_Shield), 
which I open-sourced 9 years ago.

For The Small Shield is grimdark, so the QA pairs may be grimdark. 

The system role in the training files contains the only words 
I wrote in the dataset and are intended to make the model just darkish.

I've used this to fine-tune a Llama model because I like the ChatML 
template and Claude suggested it.

I'll be publishing a model fine-tuned with this data. The dataset and model are a 
happy mess as one would expect from me instructing an LLM to turn each 
first-draft chapter into QA pairs, then adding more QA pairs to normalize 
direction, gods, and magic, having since used the first draft for my own lore. 

I created this dataset to advertise my ability to be creative with data. 
I also created this data to explore how to train an LLM to be creative rather
than a killing machine. I do not believe any LLM will be more creative than 
you. So write and know your creative worth. And if you need a place to begin to 
edit and write into an LLM, then change this training data to fine-tune that LLM, 
and please cite your sources. It does tell a story from beginning to end. 
Someday I may release the chapter data I'll be using for GraphRAG on the model
this dataset fine-tunes.

I am writing my world with dioramas and stop motion stories: https://www.instagram.com/ofthesmallshield/

I should note that the words that follow these words were not written by me, Kalab J. Oster.


## Contents

**2,001** records, provided in two interchangeable formats:

- `data/train-00000-of-00001.parquet` — canonical (powers the Dataset Viewer and `load_dataset`).
- `data/train.jsonl` — the same records as human-readable JSON Lines (the source file; the Parquet is generated from it).

### Schema

Each line is a JSON object with three fields:

```json
{
  "system": "You are Carlos, the Barded Dwarf ... universe of Rise&Set ...",
  "input":  "<user turn / prompt>",
  "output": "<Carlos's in-character response>"
}
```

- **system** — the character/system prompt establishing Carlos's voice and the
  Rise&Set world constraints (consistent across records).
- **input** — the user question or story prompt.
- **output** — the target in-character completion.

## Loading

```python
from datasets import load_dataset

ds = load_dataset("wordsum/for-the-small-shield-instruct")
print(ds["train"][0])
```

To reproduce the training format (ChatML), concatenate the fields as:
`<|im_start|>system\n{system}<|im_end|>\n<|im_start|>user\n{input}<|im_end|>\n<|im_start|>assistant\n{output}<|im_end|>`

## Source & attribution

This dataset combines human-authored and AI-generated material, attributed as
follows:

- **Source text — written by kalaboster.** The records are derived from the
  first-draft novel *For The Small Shield*, an original work written by
  kalaboster: <https://github.com/wordsum/For_The_Small_Shield>
- **`system` field — written by kalaboster.** The system/character prompt (which
  defines Carlos's voice and the Rise&Set world constraints) was authored by
  kalaboster.
- **`input` / `output` QA pairs — generated by Claude (Anthropic).** kalaboster
  used Claude to transform the first-draft novel above into instruction-style
  question/answer pairs. The QA content is therefore AI-generated from the
  human-authored source novel.

In short: **kalaboster** wrote the novel and the system prompt; **Claude** turned
that novel into the QA pairs.

## Dataset creation

- **Purpose:** teach the model Carlos's voice plus canonical world facts
  (characters, places, items, lore) so answers stay in-universe.
- **Process:** the first-draft novel was written by hand, then passed through
  Claude to produce QA pairs grounded in the story's characters, places, items,
  and lore.
- **Splits:** single `train` split (2,001 records). Public release `v1`
  (internal build v5) of an evolving set.

## Personal & sensitive information

Fictional content set in an original fantasy universe; contains no personal or
private data. Confirm you hold the rights to the underlying story material before
publishing.

## Licensing & rights

The source novel is kalaboster's original work, and the QA pairs are Claude
(Anthropic) outputs generated from it — under Anthropic's terms, the user retains
rights to those outputs. Pick terms you're comfortable with for the combined
dataset — a non-commercial Creative Commons license (`cc-by-nc-4.0`) is a common
default for creative training data, but set it to whatever matches your intent.

## Citation

```bibtex
@misc{for-the-small-shield-instruct-v1-2026,
  title  = {For The Small Shield --- Training Data (v1)},
  author = {kalaboster},
  year   = {2026},
  url    = {https://huggingface.co/datasets/wordsum/for-the-small-shield-instruct}
}
```

---

*Most This dataset card was drafted by Claude (Anthropic), model `claude-opus-4-8`, on 2026-08-04. The training data is the author's own work.*