build-small-hackathon/compliment-forest-sft

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.