104 lines
4.0 KiB
Markdown
104 lines
4.0 KiB
Markdown
---
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language: en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- text-generation
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- qwen2
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- kid-persona
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- child-speech
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- childes
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- child-development
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- research
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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datasets:
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- manjushv/childes-kid-persona-3-4
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---
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# Kid Persona Model (Age 3-4)
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A fine-tuned LLM that generates realistic child speech patterns for ages 3-4, trained on real child utterances from the CHILDES research corpus.
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## What This Is
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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](https://childes.talkbank.org/) (Child Language Data Exchange System) — the world's largest database of child language development, spanning 40+ years of recorded parent-child conversations.
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## Why It Exists
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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:
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- **Testing children's voice/chat AI** — simulate realistic child inputs
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- **Child development research** — study language patterns programmatically
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- **EdTech development** — build products that handle real child speech
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- **Speech therapy tools** — generate age-appropriate test cases
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## Examples
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| Adult says | Model responds | Why it's realistic |
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|-----------|---------------|-------------------|
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| "What color is the sky?" | "it's green!" | 3-year-olds give wrong answers |
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| "Can you count to five?" | "um... uh... five!" | Skips to the end with fillers |
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| "Who is this man?" | "crazy" | One-word, concrete answers |
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| "What time is supper?" | "at my house?" | Answers WHERE instead of WHEN |
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| "Do you want juice?" | "yeah!" | Simple affirmative |
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| "Can you describe the snowman?" | "I don't know I can" | Inverted grammar ("if" → missing) |
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## Training Details
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- **Base model**: Qwen/Qwen2.5-1.5B-Instruct
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- **Method**: QLoRA (r=16, alpha=32, all-linear targets)
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- **Data**: 50,000 real child utterances from CHILDES (ages 2-4)
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- **Training**: 1 epoch, 28 minutes on A100
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- **Cost**: Under $2
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- **Loss**: 4.12 → 1.80
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## Usage
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```python
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from transformers import pipeline
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pipe = pipeline("text-generation", model="manjushv/kid-persona-young-3-4-merged")
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result = pipe(
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"<|im_start|>system\nYou are a 3-year-old child. Respond naturally.<|im_end|>\n"
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"<|im_start|>user\nDo you want to play?<|im_end|>\n"
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"<|im_start|>assistant\n",
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max_new_tokens=20, temperature=0.9, do_sample=True,
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)
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print(result[0]["generated_text"])
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```
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## Live Demo
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Try it: [Kid Persona Inference Space](https://huggingface.co/spaces/manjushv/kid-persona-inference)
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## ⚠️ Important Disclaimer
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**This model simulates child speech patterns. It is NOT a model for children to interact with.**
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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.
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**This model should NOT be used to:**
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- Interact with real children
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- Replace child safety systems
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- Generate content targeting children
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- Train models that interact with children without additional safety layers
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**This model IS designed for:**
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- Testing and evaluating children's tech products
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- Research into child language development
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- Generating realistic test cases for EdTech/voice AI
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- Understanding age-specific speech patterns
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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.
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## Data Source
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[CHILDES](https://childes.talkbank.org/) — Child Language Data Exchange System
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- License: CC-BY 4.0
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- Citation: MacWhinney, B. (2000). The CHILDES Project: Tools for Analyzing Talk. 3rd Edition. Mahwah, NJ: Lawrence Erlbaum Associates.
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## Built By
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[Minie AI](https://minie.ai) — Building voice-first AI experiences for children.
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