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---
language:
- en
license: apache-2.0
tags:
- story
- young children
- educational
- knowledge
base_model: mistralai/Mistral-7B-v0.1
datasets:
- ajibawa-2023/Children-Stories-Collection
model-index:
- name: Young-Children-Storyteller-Mistral-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 68.69
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 84.67
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.11
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 62.62
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 81.22
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.2
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B
name: Open LLM Leaderboard
---
**Young-Children-Storyteller-Mistral-7B**
This model is based on my dataset [Children-Stories-Collection](https://huggingface.co/datasets/ajibawa-2023/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](https://huggingface.co/MarsupialAI/Young-Children-Storyteller-Mistral-7B_iMatrix_GGUF/tree/main)
Exllama: TBA
Special Thanks to [MarsupialAI](https://huggingface.co/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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Young-Children-Storyteller-Mistral-7B)
| 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|
|