--- 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 ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/J48WYa1qmKnRaILA_44Ao.jpeg) Example 2 ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/H2FucX0CTtV25wlgHmifN.jpeg) Example 3 ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/o7hiMI5noO8fPedUG75H8.jpeg) # [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|