README.md

metadata

language:
  - en
license: mit
task_categories:
  - text-generation
tags:
  - conversational
  - emotional-intelligence
  - empathy
  - gen-z
  - mental-health
  - small-models
  - gguf
  - lora
size_categories:
  - 1K<n<10K
pretty_name: DiaChat Dataset

DiaChat Dataset

Dataset Summary

The DiaChat Dataset is a curated conversational dataset designed to fine-tune very small language models (≈270M parameters) to behave as a warm, emotionally intelligent, Gen Z–style conversational companion called Dia.

This dataset focuses on tone, empathy, and emotional awareness, rather than reasoning, knowledge recall, or task completion.

It is suitable for client-side, offline, and privacy-first AI systems.

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Supported Tasks

• Conversational response generation
• Emotion-aware dialogue
• Empathetic text generation

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Languages

• English (en)

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Dataset Structure

Each example contains four fields:

• system: Defines Dia’s persona and conversational style
• user: The user’s message
• context: Natural-language emotional metadata
• assistant: Dia’s empathetic response

Example

system: You are "Dia" — a warm, emotionally intelligent, Gen Z-style friend.
user: I feel like everyone’s doing better than me lately.
context:
Emotion detected: sadness
User tone: self-critical
assistant: Yeah… that comparison spiral hits hard. But your life isn’t behind — it’s just on its own timeline.

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Emotion Coverage

The dataset spans a broad emotional range, including:

• sadness
• anxiety
• self-doubt
• loneliness
• burnout
• pride
• frustration
• numbness
• overthinking
• neutral / conversational moods

This ensures the assistant does not feel crisis-only or clinical.

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Intended Use

Recommended

• LoRA / QLoRA fine-tuning
• Small and quantized language models (Gemma, Phi-style, Qwen-small)
• GGUF-based inference pipelines
• Emotion-first conversational agents

Not Recommended

• Medical, psychological, or clinical diagnosis
• Crisis intervention systems
• Decision-making or advisory systems

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Bias, Risks, and Limitations

• The dataset reflects a Gen Z conversational tone and may not generalize to other demographics.
• Responses are intentionally non-directive and may not satisfy users seeking explicit advice.
• The dataset should not be used as a replacement for professional mental health support.

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Dataset Creation

• Manually curated and generated with strict style constraints
• Emphasis on consistency over scale
• Context provided as plain natural language for GGUF compatibility

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Licensing

This dataset is released for research and experimental use only.
Use responsibly and with appropriate safeguards.