ajibawa-2023/Young-Children-Storyteller-Mistral-7B

Young-Children-Storyteller-Mistral-7B

This model is based on my dataset Children-Stories-Collection which has over 0.9 million stories meant for Young Children (age 6 to 12).

Drawing upon synthetic datasets meticulously designed with the developmental needs of young children in mind, Young-Children-Storyteller is more than just a tool—it's a companion on the journey of discovery and learning. With its boundless storytelling capabilities, this model serves as a gateway to a universe brimming with wonder, adventure, and endless possibilities.

Whether it's embarking on a whimsical adventure with colorful characters, unraveling mysteries in far-off lands, or simply sharing moments of joy and laughter, Young-Children-Storyteller fosters a love for language and storytelling from the earliest of ages. Through interactive engagement and age-appropriate content, it nurtures creativity, empathy, and critical thinking skills, laying a foundation for lifelong learning and exploration.

Rooted in a vast repository of over 0.9 million specially curated stories tailored for young minds, Young-Children-Storyteller is poised to revolutionize the way children engage with language and storytelling.

Kindly note this is qLoRA version, another exception.

GGUF & Exllama

Standard Q_K & GGUF: Link

Exllama: TBA

Special Thanks to MarsupialAI for quantizing the model.

Training

Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took more than 30 Hours. Axolotl codebase was used for training purpose. Entire data is trained on Mistral-7B-v0.1.

Example Prompt:

This model uses ChatML prompt format.

<|im_start|>system
You are a Helpful Assistant who can write educational stories for Young Children.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

You can modify above Prompt as per your requirement.

I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.

Thank you for your love & support.

Example Output

Example 1

Example 2

Example 3

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	71.08
AI2 Reasoning Challenge (25-Shot)	68.69
HellaSwag (10-Shot)	84.67
MMLU (5-Shot)	64.11
TruthfulQA (0-shot)	62.62
Winogrande (5-shot)	81.22
GSM8k (5-shot)	65.20