Instructions to use manjushv/kid-persona-young-3-4-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use manjushv/kid-persona-young-3-4-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="manjushv/kid-persona-young-3-4-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("manjushv/kid-persona-young-3-4-merged")
model = AutoModelForCausalLM.from_pretrained("manjushv/kid-persona-young-3-4-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use manjushv/kid-persona-young-3-4-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "manjushv/kid-persona-young-3-4-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "manjushv/kid-persona-young-3-4-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/manjushv/kid-persona-young-3-4-merged

SGLang

How to use manjushv/kid-persona-young-3-4-merged with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "manjushv/kid-persona-young-3-4-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "manjushv/kid-persona-young-3-4-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "manjushv/kid-persona-young-3-4-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "manjushv/kid-persona-young-3-4-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use manjushv/kid-persona-young-3-4-merged with Docker Model Runner:
```
docker model run hf.co/manjushv/kid-persona-young-3-4-merged
```

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.

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Safetensors

Model size

2B params

Tensor type

F16

Model tree for manjushv/kid-persona-young-3-4-merged

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct