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 toolβit'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 |
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Base model
mistralai/Mistral-7B-v0.1Dataset used to train ajibawa-2023/Young-Children-Storyteller-Mistral-7B
Spaces using ajibawa-2023/Young-Children-Storyteller-Mistral-7B 3
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard68.690
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard84.670
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.110
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard62.620
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard81.220
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard65.200