---
license: apache-2.0
language:
- en
task_categories:
- text-generation
pretty_name: Compliment Forest SFT
size_categories:
- 1K<n<10K
tags:
- minicpm
- structured-generation
- synthetic-data
- positive-reframing
- build-small-hackathon
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
---

# Compliment Forest SFT

Compliment Forest SFT teaches a small language model to turn a `(name, situation)`
pair into a strict JSON forest of grounded encouragement. Each forest contains five
distinct creature-strength clearings, a situation-specific line, an agency-oriented
reflection, a short first-person spell, and a creature-only image prompt.

## Dataset Size

- Train: 1,350 records
- Validation: 150 records
- Seed: 42
- Language: English

Every row contains:

- `name`
- `situation`
- `source`
- `teacher_model`
- `messages`: MiniCPM-compatible system, user, and assistant chat messages

## Construction

The released v1 records come from a deterministic synthetic formatting layer over
50 hand-authored situations spanning work, study, moving, relationships, parenting,
creative work, wellbeing, grief-adjacent moments, money, social situations, and
leadership.

The data pipeline was informed and tested against:

- `SALT-NLP/positive_reframing` for growth, optimism, neutralizing, and impermanence
  framing patterns.
- `Estwld/empathetic_dialogues_llm` for situation-grounded supportive register.
- `asuender/motivational-quotes` for short mantra analysis.

Cohere Command A was used during pilot synthetic generation. Its pilot outputs were
schema-validated and filtered, but no Cohere row passed the stricter final v1
groundedness gate. The published v1 rows therefore use `source=template_coverage`
and have no teacher-model dependency.

## Validation

The builder rejects:

- invalid or extra JSON fields;
- fewer than three or more than six proposed strengths;
- blank or overlong fields;
- spells that do not begin in the first person or exceed 12 words;
- crisis or acute-risk inputs;
- any clearing line that does not repeat at least one concrete situation term;
- duplicate `(name, situation)` identities.

The validation split is deterministic and disjoint.

## Intended Use

This dataset is intended for supervised fine-tuning and evaluation of
`openbmb/MiniCPM5-1B` for The Compliment Forest hackathon project. It may also be
useful for experiments in small-model JSON adherence and supportive rewriting.

## Limitations

The records are synthetic and structurally more regular than natural writing. They
are not clinical advice, therapy data, or a substitute for safety evaluation. The
runtime application adds a separate crisis guard and author-critic quality pass.