4.0 KiB
language, license, library_name, pipeline_tag, tags, base_model, datasets
| language | license | library_name | pipeline_tag | tags | base_model | datasets | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| en | apache-2.0 | transformers | text-generation |
|
Qwen/Qwen2.5-1.5B-Instruct |
|
Kid Persona Model (Age 3-4)
A fine-tuned LLM that generates realistic child speech patterns for ages 3-4, trained on real child utterances from the CHILDES research corpus.
What This Is
This model simulates how 3-4 year old children actually talk. It was fine-tuned on 50,000 real child utterances from the CHILDES corpus (Child Language Data Exchange System) — the world's largest database of child language development, spanning 40+ years of recorded parent-child conversations.
Why It Exists
Every children's tech product tests with adults pretending to be kids. Adults type full sentences with perfect grammar. Real 3-year-olds say "me want dat cookie" and "her's gonna make." This model bridges that gap for:
- Testing children's voice/chat AI — simulate realistic child inputs
- Child development research — study language patterns programmatically
- EdTech development — build products that handle real child speech
- Speech therapy tools — generate age-appropriate test cases
Examples
| Adult says | Model responds | Why it's realistic |
|---|---|---|
| "What color is the sky?" | "it's green!" | 3-year-olds give wrong answers |
| "Can you count to five?" | "um... uh... five!" | Skips to the end with fillers |
| "Who is this man?" | "crazy" | One-word, concrete answers |
| "What time is supper?" | "at my house?" | Answers WHERE instead of WHEN |
| "Do you want juice?" | "yeah!" | Simple affirmative |
| "Can you describe the snowman?" | "I don't know I can" | Inverted grammar ("if" → missing) |
Training Details
- Base model: Qwen/Qwen2.5-1.5B-Instruct
- Method: QLoRA (r=16, alpha=32, all-linear targets)
- Data: 50,000 real child utterances from CHILDES (ages 2-4)
- Training: 1 epoch, 28 minutes on A100
- Cost: Under $2
- Loss: 4.12 → 1.80
Usage
from transformers import pipeline
pipe = pipeline("text-generation", model="manjushv/kid-persona-young-3-4-merged")
result = pipe(
"<|im_start|>system\nYou are a 3-year-old child. Respond naturally.<|im_end|>\n"
"<|im_start|>user\nDo you want to play?<|im_end|>\n"
"<|im_start|>assistant\n",
max_new_tokens=20, temperature=0.9, do_sample=True,
)
print(result[0]["generated_text"])
Live Demo
Try it: Kid Persona Inference Space
⚠️ Important Disclaimer
This model simulates child speech patterns. It is NOT a model for children to interact with.
This model generates responses as a child would — including saying "yeah" to any question, giving wrong answers, and using incorrect grammar. This is by design: real 3-year-olds respond this way.
This model should NOT be used to:
- Interact with real children
- Replace child safety systems
- Generate content targeting children
- Train models that interact with children without additional safety layers
This model IS designed for:
- Testing and evaluating children's tech products
- Research into child language development
- Generating realistic test cases for EdTech/voice AI
- Understanding age-specific speech patterns
The training data comes from the CHILDES corpus, which contains recordings of real parent-child interactions collected under institutional review board (IRB) approval for research purposes.
Data Source
CHILDES — Child Language Data Exchange System
- License: CC-BY 4.0
- Citation: MacWhinney, B. (2000). The CHILDES Project: Tools for Analyzing Talk. 3rd Edition. Mahwah, NJ: Lawrence Erlbaum Associates.
Built By
Minie AI — Building voice-first AI experiences for children.